diff --git a/docs/HYPERTWIST_SPEECH_ROW_SOURCE_LIVE_STATE_RECONCILIATION_2026-05-27.md b/docs/HYPERTWIST_SPEECH_ROW_SOURCE_LIVE_STATE_RECONCILIATION_2026-05-27.md
new file mode 100644
index 0000000..b78fff2
--- /dev/null
+++ b/docs/HYPERTWIST_SPEECH_ROW_SOURCE_LIVE_STATE_RECONCILIATION_2026-05-27.md
@@ -0,0 +1,97 @@
+# Created on `2026-05-27`
+
+## Status
+
+`closed`
+
+## Purpose
+
+This note backfills the `v6.3` row-source live-state and reset-lane fields for
+the four later-landed speech-family rows that already have explicit live speech
+canon but were left blank in the source pack:
+
+- `ggml-org/whisper.cpp`
+- `SYSTRAN/faster-whisper`
+- `rhasspy/piper`
+- `coqui-ai/TTS`
+
+This is a row-source reconciliation pass only. It does not reopen speech
+product code.
+
+## Current judgment
+
+- no previously landed speech packet needs reopening
+- no product code change is justified from this pass
+- the row-source pack should not leave these live rows with blank live-state or
+  reset-lane fields
+
+## Current speech truth
+
+The landed speech-family code-side packets are already explicit:
+
+- `ggml-org/whisper.cpp`
+  - `Phase 6R-E` speech transcript session boundary
+  - `Phase 6R-U` live microphone capture shell
+  - `Phase 6R-V` device-permission and capture-route readiness shell
+  - `Phase 6R-W` downloadable model and payload custody boundary
+- `SYSTRAN/faster-whisper`
+  - `Phase 6R-F` transcription-service orchestration profile
+- `rhasspy/piper`
+  - `Phase 6R-G` local narration sidecar
+- `coqui-ai/TTS`
+  - `Phase 6R-H` advanced narration orchestration
+
+## Row-source backfill
+
+Use these `v6.3` source-pack calls:
+
+### `ggml-org/whisper.cpp`
+
+- live state: `implemented_live_permissive`
+- reset lane: `landed_permissive_preserve`
+- next-step truth:
+  - `Phase 6R-E`, `6R-U`, `6R-V`, and `6R-W` are closed
+  - preserve the landed bounded native speech-session, microphone-shell,
+    permission-readiness, and payload-custody lane
+  - keep broader payload shipping and model-license review separate
+
+### `SYSTRAN/faster-whisper`
+
+- live state: `implemented_live_permissive`
+- reset lane: `landed_permissive_preserve`
+- next-step truth:
+  - `Phase 6R-F` is closed
+  - preserve the landed complementary Python transcription-orchestration lane
+    above the existing speech session boundary
+  - do not widen it into top-level provider/session ownership
+
+### `rhasspy/piper`
+
+- live state: `implemented_live_permissive`
+- reset lane: `landed_permissive_preserve`
+- next-step truth:
+  - `Phase 6R-G` is closed
+  - preserve the landed bounded local narration sidecar lane
+  - keep broad voice-model and payload review separate
+
+### `coqui-ai/TTS`
+
+- live state: `implemented_live_boundary_sensitive`
+- reset lane: `landed_boundary_sensitive_preserve`
+- next-step truth:
+  - `Phase 6R-H` is closed
+  - preserve the landed bounded advanced narration orchestration lane under the
+    current `MPL` boundary doctrine
+  - keep model, voice, and payload review separate from the code-license
+    judgment
+
+## Required canon posture
+
+Current authority surfaces should now say all of the following consistently:
+
+- these four rows are already live in current speech canon
+- these four rows should not have blank `v6.3` live-state or reset-lane fields
+- `whisper.cpp`, `faster-whisper`, and `piper` are live bounded permissive
+  lanes
+- `coqui-ai/TTS` is a live bounded `MPL`-side lane and should be treated as
+  boundary-sensitive in the row-source pack
diff --git a/docs/ops/HYPERTWIST_IMPLEMENTATION_PHASE_1_KICKOFF.md b/docs/ops/HYPERTWIST_IMPLEMENTATION_PHASE_1_KICKOFF.md
index 3a6130a..9271ab2 100644
--- a/docs/ops/HYPERTWIST_IMPLEMENTATION_PHASE_1_KICKOFF.md
+++ b/docs/ops/HYPERTWIST_IMPLEMENTATION_PHASE_1_KICKOFF.md
@@ -39,6 +39,10 @@ Current verified product-side reality is:
 - the landed `coqui-ai/TTS` code-side slice is not a clean-room lane by
   current doctrine; model and payload review remains separate from the package
   code license
+- the `2026-05-27` speech row-source live-state reconciliation note is the
+  authority for backfilling the `v6.3` source-pack live-state and reset-lane
+  fields for `ggml-org/whisper.cpp`, `SYSTRAN/faster-whisper`,
+  `rhasspy/piper`, and `coqui-ai/TTS`
 - the dedicated current doctrine note for `MPL`-side non-`GPL` usage is:
   - [HYPERTWIST_MPL_BOUNDARY_AND_NON_GPL_USAGE_DOCTRINE_2026-05-25.md](C:/HyperTwist/docs/ops/HYPERTWIST_MPL_BOUNDARY_AND_NON_GPL_USAGE_DOCTRINE_2026-05-25.md:1)
 - the dedicated release-placement checklist for selling or externally
diff --git a/docs/repo_portfolio_unified_copyleft_strategy_matrix_v6_3.csv b/docs/repo_portfolio_unified_copyleft_strategy_matrix_v6_3.csv
index 09921e6..5f01af0 100644
--- a/docs/repo_portfolio_unified_copyleft_strategy_matrix_v6_3.csv
+++ b/docs/repo_portfolio_unified_copyleft_strategy_matrix_v6_3.csv
@@ -1,15 +1,15 @@
 "repo","primary_url","best_fit_project_v2","phase_g_bucket","portfolio_role_v3","recommended_action_v2","repurposing_potential_v2","v6_license_annotation","v6_license_annotation_status","v6_license_annotation_source","copyleft_relevance_v6_1","copyleft_strategy_v6_1","copyleft_rationale_v6_1","copyleft_strategy_confidence_v6_1","copyleft_manual_review_trigger_v6_1","v6_3_source_of_truth","v6_3_live_state_2026_05_11","v6_3_reset_lane_2026_05_11","v6_3_reset_next_step_2026_05_11"
 "HactarCE/Hyperspeedcube","https://github.com/HactarCE/Hyperspeedcube","HyperTwist","","locked core candidate","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","high","strategic-or-implemented-component","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-A/K/L/M/N preserve sequence closed. Preserve as the landed first-party hyper puzzle catalog, notation, replay-log serialization, replay verification, stats-shape, solve-record, and puzzle-DSL authoring lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, not from the old candidate queue wording."
-"SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","","donor bench","repurpose","moderate modification","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","high","model-artifact-review-required","v6.3_final_source_of_truth","","",""
+"SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","","donor bench","repurpose","moderate modification","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","high","model-artifact-review-required","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Phase 6R-F is closed. Preserve as the landed complementary Python transcription-orchestration lane above the existing speech session boundary; do not widen it into top-level provider/session ownership."
 "cubing/alg.js","https://github.com/cubing/alg.js","HyperTwist","Donor Bench","donor bench","repurpose","architecture only","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve the parser/AST/traversal semantics through a scrubbed Model A handoff only.","high","gpl-clean-room-donor","v6.3_final_source_of_truth","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only algorithm-language candidate; implement only from the scrubbed Model A dossier and retained-set contract."
 "cubing/twisty.js","https://github.com/cubing/twisty.js","HyperTwist","Donor Bench","donor bench","repurpose","architecture only","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve viewer/player shell, scrubber, and twisty-element behavior through a scrubbed Model A handoff only.","high","gpl-clean-room-donor","v6.3_final_source_of_truth","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only embedded viewer candidate; implement only from the scrubbed Model A dossier and retained-set contract."
 "HactarCE/2x2x2x2-Scrambler","https://github.com/HactarCE/2x2x2x2-Scrambler","HyperTwist","Donor Bench","donor bench","repurpose","architecture only","GPL-3.0","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0 and the source explicitly notes a ported lineage from an earlier scrambler. Retain it only as a focused clean-room donor target and implement any valuable behavior through a scrubbed first-party specification.","high","gpl-clean-room-donor","v6.3_final_source_of_truth","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only Melinda 2x2x2x2 candidate; keep copied-port lineage explicit and implement only from the scrubbed Model A dossier."
 "kkoomen/qbr","https://github.com/kkoomen/qbr","HyperTwist","","locked core candidate","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","high","strategic-or-implemented-component","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-B/O preserve sequence closed for the currently justified slice. Preserve as the landed first-party recognition calibration, ordered face-observation, and webcam-shell lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while keeping multilingual/font redistribution and broader solve-shell ownership deferred."
-"coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","","donor bench","repurpose","moderate modification","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","medium","model-license-selection-required","v6.3_final_source_of_truth","","",""
+"coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","","donor bench","repurpose","moderate modification","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","medium","model-license-selection-required","v6.3_final_source_of_truth","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 6R-H is closed. Preserve as the landed bounded advanced narration orchestration lane under the current MPL boundary doctrine; keep model, voice, and payload review separate from the code-license judgment."
 "vivaansinghvi07/rubix-cube-solver","https://github.com/vivaansinghvi07/rubix-cube-solver","HyperTwist","Locked Parallel Foundation","locked core candidate","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","high","strategic-or-implemented-component","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-C/P/Q preserve sequence closed for the currently justified slice. Preserve as the landed first-party committed-face reconstruction, browser/webcam shell, and bounded solve-explanation/recommendation lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while broad solver-backend ownership and bundled twistysim.min.js redistribution stay deferred."
-"ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","","donor bench","repurpose","moderate modification","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","high","model-artifact-review-recommended","v6.3_final_source_of_truth","","",""
+"ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","","donor bench","repurpose","moderate modification","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","high","model-artifact-review-recommended","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Phase 6R-E, Phase 6R-U, Phase 6R-V, and Phase 6R-W are closed. Preserve as the landed bounded native speech-session, microphone-shell, permission-readiness, and payload-custody lane; keep broader payload shipping and model-license review separate."
 "tao-yu/Alg-Trainer","https://github.com/tao-yu/Alg-Trainer","HyperTwist","","locked core candidate","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","high","strategic-or-implemented-component","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Phase 1R closed. Preserve as a landed first-party permissive lane; keep notices and attribution visible and widen only through ordinary owned enhancement work."
-"rhasspy/piper","https://github.com/rhasspy/piper","multi-project","","donor bench","repurpose","moderate modification","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","high","voice-artifact-review-required","v6.3_final_source_of_truth","","",""
+"rhasspy/piper","https://github.com/rhasspy/piper","multi-project","","donor bench","repurpose","moderate modification","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","high","voice-artifact-review-required","v6.3_final_source_of_truth","implemented_live_permissive","landed_permissive_preserve","Phase 6R-G is closed. Preserve as the landed bounded local narration sidecar lane; keep broad voice-model and payload review separate."
 "cubing/cubing.js","https://github.com/cubing/cubing.js","HyperTwist","Locked Strategic Donor","locked strategic donor","integrate","direct","MPL-2.0 OR GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","The repo is dual-licensed MPL-2.0 OR GPL-3.0-or-later. HyperTwist can consume it as a package or bounded adapter under the MPL side, but should avoid a carefree deep private source fork of upstream files.","high","dual-license-boundary-review","v6.3_final_source_of_truth","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 4R-A closed. Preserve as the landed first-party classic-cubing semantic/runtime adapter lane through explicit MPL-aware dependency or adapter use; keep notices and publication duties explicit before any future direct upstream file modification."
 "cahidenes/rubiks-cube-solver","https://github.com/cahidenes/rubiks-cube-solver","HyperTwist","Locked Strategic Donor","locked strategic donor","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","high","strategic-or-implemented-component","v6.3_final_source_of_truth","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow face-placement, cube-string assembly, and optional two-opposite-corner comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
 "tentone/rubix-solver","https://github.com/tentone/rubix-solver","HyperTwist","Locked Strategic Donor","locked strategic donor","integrate","direct","MIT","known_from_reference_material","uploaded_reference_docs","permissive_or_noncopyleft_known","direct_incorporation_ok","Practical working assumption remains MIT from README and repo presentation, but the checked mirror lacks a bundled top-level license file. Keep this row permissive-active only as a subordinate recognition comparison adjunct, and capture the final authoritative upstream license text before any direct vendoring.","medium","readme-only-license-capture-before-direct-vendoring","v6.3_final_source_of_truth","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow native quad-sorting, square-mask color sampling, center-color face labeling, and face-state comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
diff --git a/docs/repo_portfolio_unified_operational_v6_3.csv b/docs/repo_portfolio_unified_operational_v6_3.csv
index 549d9c5..2af0f21 100644
--- a/docs/repo_portfolio_unified_operational_v6_3.csv
+++ b/docs/repo_portfolio_unified_operational_v6_3.csv
@@ -1,15 +1,15 @@
 "execution_queue_order","repo","primary_url","best_fit_project_v2","project_rank","portfolio_priority_score","execution_priority_score_v3","priority_band","taxonomy_hardened","archetype_primary","archetype_secondary","portfolio_role_v3","recommended_action_v2","repurposing_potential_v2","modification_scope_detail_v3","capability_extract","integration_realization_detail","consolidation_detail","repurpose_detail","realization_checklist","inspection_depth_recommendation","hidden_value_hypothesis","source_inspection_questions","source_code_audit_targets","coding_model_instruction_v3","merger_partner_1","merger_type_1","merger_rationale_1","merger_partner_2","merger_type_2","merger_rationale_2","merger_partner_3","merger_type_3","merger_rationale_3","cross_project_transfer_targets","cross_project_transfer_rationale","exclusion_discipline","exclusion_reasoning_v3","confidence_v3","confidence_rationale_v3","confidence_reassessed","confidence_rationale","licensing_filter_status","bookmark_project_sections","bookmark_heading_paths_top","bookmark_description_sample","memo_signal_sample","expanded_reasoning","source_attachments","in_bookmarks","in_memo","bookmark_occurrences","memo_mentions","cluster_tag","source_audit_packet_id","category_guess_v2","reasoning","best_fit_project","category_guess","recommended_action","repurposing_potential","confidence","_repo_norm","v5_primary_eval_project","v5_runtime_project","v5_scriptorium_override_status","v5_scriptorium_current_reality_status","v5_scriptorium_bucket","v5_scriptorium_stack_layer","v5_scriptorium_source_audit_priority","v5_scriptorium_actual_role","v5_scriptorium_integration_boundary","v5_scriptorium_evidence_summary","v5_scriptorium_design_language_notes","v5_scriptorium_recommended_context_packet","v5_scriptorium_supersedes_prior_assessment","v5_scriptorium_bucket_rank","v5_scriptorium_prio_rank","v5_scriptorium_sort_score","v5_source_of_truth","v6_license_annotation","v6_license_annotation_status","v6_license_annotation_source","v6_supplemental_intake_present","v6_supplemental_source_groups","v6_supplemental_source_sections","v6_supplemental_source_files","v6_reference_material_position","v6_kali_agent_access_relevance","v6_branch_seed_prompt_included","v6_branch_seed_scope","v6_intake_wave","v6_notes","project_rank_num","audit_rank_num","priority_num","phase_g_bucket","phase_g_project_stack_layer","phase_g_inclusion_status","phase_g_source_audit_priority","copyleft_relevance_v6_1","copyleft_strategy_v6_1","copyleft_rationale_v6_1","preferred_boundary_model_v6_1","open_compliance_if_used_as_is_v6_1","reverse_engineer_if_proprietary_core_needed_v6_1","copyleft_strategy_confidence_v6_1","copyleft_manual_review_trigger_v6_1","as_is_incorporation_sensible_v6_1","v6_2_sre_layer","v6_2_sre_stratum","v6_2_sre_role","v6_2_sre_family","v6_2_related_kali_package","v6_2_related_upstream_repo","v6_2_kali_package_suffices_for_tool_execution","v6_2_upstream_repo_preferred_for_deep_eval","v6_2_index_page_followup_useful","v6_2_index_page_followup_reason","v6_2_sre_notes","v6_2_dnspy_ilspy_relevance","v6_3_source_of_truth","v6_3_merge_note","v6_3_live_state_2026_05_11","v6_3_reset_lane_2026_05_11","v6_3_reset_next_step_2026_05_11"
 "7701","HactarCE/Hyperspeedcube","https://github.com/HactarCE/Hyperspeedcube","HyperTwist","1.0","160.0","193.0","A","hypercubing / nD engine","foundation engine","simulation donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving generalized puzzle/state/render logic while building a new application shell around it.","HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Exten...","Integrate as a hypercubing / nD simulation subsystem for HyperTwist. Preserve the strongest existing pieces — nD state model, move notation, renderer, projection controls, solver/traversal logic, puzzle serialization, replay — and expose them behind a portfolio-stable interface. Wire first into cubing/cubing.js, then into tao-yu/Alg-Trainer for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: cubing/cubing.js, tao-yu/Alg-Trainer, poliva/cubedex.","Repurpose here means: turn it into a higher-dimensional renderer/simulator donor and shared interaction grammar for HyperTwist and long-horizon VectorShell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate puzzle/state core; Extract render/input abstractions; Document notation and save format","full subsystem extraction review","Hidden value is likely in generalized puzzle/state representations, higher-dimensional transforms, notation systems, save formats, puzzle generators, and rendering abstractions.","Inspect generalized puzzle/state model; notation parser; transform math; rendering abstraction; save/load format; puzzle generator; input mapping; performance optimizations.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for higher-dimensional state/notation representations, projection math, renderer abstractions, puzzle serialization, controls, and replay/training hooks.","Audit HactarCE/Hyperspeedcube as a hypercubing / nD engine candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect generalized puzzle/state model; notation parser; transform math; rendering abstraction; save/load format; puzzle generator; input mapping; performance optimizations. Decide whether the best extraction path is direct and whether it belongs as foundation engine / simulation donor. Test the three merger paths in order: 1) cubing/cubing.js [3D engine + notation/state donor]; 2) tao-yu/Alg-Trainer [training UX donor]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","cubing/cubing.js","3D engine + notation/state donor","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","tao-yu/Alg-Trainer","training UX donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 6","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Extended Comprehensive Open-Source Building Blocks (April 2026)Updated exhaustive scan — focused on high-value for your codebase (simulators, trainers, vision, hyper).MIT or Apache Licensed (Fully permissive — fork/abs","HactarCE/Hyperspeedcube is treated as integrate for HyperTwist because visible metadata points to the hypercubing / nD simulation layer. Surface signal: HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Exten... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","6.0","HT_hyper_engine","HT_hyper_engine_0001","hypercubing / nD simulation","Integrate as a shared building block across at least two projects. Its memo and bookmark signals place it in the hypercubing / nD simulation layer.","multi-project","hypercubing / nD simulation","integrate","direct","medium","hactarce/hyperspeedcube","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","Existing v5 row reaffirmed or widened by v6 supplemental intake.","1.0","5","160.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-A/K/L/M/N preserve sequence closed. Preserve as the landed first-party hyper puzzle catalog, notation, replay-log serialization, replay verification, stats-shape, solve-record, and puzzle-DSL authoring lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, not from the old candidate queue wording."
-"7704","SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","1.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving parsers/graph schema/indexing while swapping layout, storage, or UX layers.","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for VAD-aware segmentation, batch transcription, timestamps, hotword and prefix conditioning, and Python service ergonomics; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: ggml-org/whisper.cpp, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a Python STT service, timestamped speech pipeline, or batch transcription donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Normalize graph schema; Decouple parser/indexer from UI; Expose query and layout services","deep source audit","Hidden value may sit in AST/indexing pipelines, call/dependency graph schema, incremental refresh, layout heuristics, graph query APIs, and serialization formats that can be lifted into VectorShell.","Inspect transcription API and dataclasses; batched inference; VAD chunking; word timestamps; hotwords and prefix conditioning; service-layer boundaries; benchmark and test coverage.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, benchmark scripts, and hidden experimental modules. Look for VAD, batch inference, timestamps, hotwords, model/runtime constraints, and service-layer boundaries.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","SYSTRAN/faster-whisper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0001","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded Python STT donor and service-layer candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","systran/faster-whisper","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","1.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"7704","SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","1.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving parsers/graph schema/indexing while swapping layout, storage, or UX layers.","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for VAD-aware segmentation, batch transcription, timestamps, hotword and prefix conditioning, and Python service ergonomics; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: ggml-org/whisper.cpp, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a Python STT service, timestamped speech pipeline, or batch transcription donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Normalize graph schema; Decouple parser/indexer from UI; Expose query and layout services","deep source audit","Hidden value may sit in AST/indexing pipelines, call/dependency graph schema, incremental refresh, layout heuristics, graph query APIs, and serialization formats that can be lifted into VectorShell.","Inspect transcription API and dataclasses; batched inference; VAD chunking; word timestamps; hotwords and prefix conditioning; service-layer boundaries; benchmark and test coverage.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, benchmark scripts, and hidden experimental modules. Look for VAD, batch inference, timestamps, hotwords, model/runtime constraints, and service-layer boundaries.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","SYSTRAN/faster-whisper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0001","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded Python STT donor and service-layer candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","systran/faster-whisper","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","1.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-F is closed. Preserve as the landed complementary Python transcription-orchestration lane above the existing speech session boundary; do not widen it into top-level provider/session ownership."
 "7705","cubing/alg.js","https://github.com/cubing/alg.js","HyperTwist","2.0","95.0","95.0","P0","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Moderate modification. Treat cubing/alg.js as a family-level donor for HyperTwist: extract the implementation layer that matches its strongest domain contribution, preserve its protocols/data models/CLI or renderer boundaries, and adapt only the surface integration needed for HyperTwist rather than rewriting it wholesale.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Verify actual capability surface, identify reusable subsystems, confirm integration boundary, test output formats, and decide whether promotion or demotion is justified after source inspection.","P0 tier audit. Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","cubing/alg.js may hide higher-value reusable components in its cubing family than its surface framing suggests; inspect internals before treating it as merely a category duplicate.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect cubing/alg.js at source level. Extract actual implemented capabilities, hidden modules, extension hooks, data models, and output contracts. Re-rank only if source evidence materially changes its donor/foundation potential for HyperTwist.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use cubing/alg.js as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use cubing/alg.js as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use cubing/alg.js as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Source audit may reveal reusable abstractions that travel beyond the initially assigned project, especially in control-plane, visualization, renderer, or document/AI pipeline layers.","Do not omit for licensing. Exclude only from current horizon if source audit shows it is purely documentation, governance, packaging noise, or a weak duplicate with no meaningful reusable subsystem.","Current placement is preliminary and based on family fit, repo naming, and source grouping rather than source code inspection.","medium","Pre-source-audit supplemental intake classification derived from source group, owner family, project fit, and project-wide design language.","medium","Will increase only after direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive; license is tracked separately only for optional review and packaging choices.","HyperTwist","HyperTwist","","Supplemental intake row added from v6 source-group expansion; reference docs may further inform merger logic.","cubing/alg.js enters the corpus through the v6 supplemental intake. It is treated as donor bench for HyperTwist because its family suggests value in hypertwist family. likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or xr/game-engine surfaces relevant to hypercube and cube-training workflows. Its current judgment is intentionally provisional and should be upgraded or downgraded only after direct source inspection.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0002","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai; preliminary classification only.","HyperTwist","puzzle_simulation_training_donor","donor candidate","moderate modification","medium","cubing/alg.js","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","","","","no","","","","v6_unified_source_of_truth","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references and prior Grok/initial-research docs used as reference, not as source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v6_supplemental_intake","New row created from supplemental intake (POO/Security/Agent/HyperTwist-ScriptoriumAI/Kali/Consider II).","2.0","1","95.0","Donor Bench","Focused restrictive clean-room donor target","","P2","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve the parser/AST/traversal semantics through a scrubbed Model A handoff only.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — preferred path for reproducing parser/AST/traversal semantics in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only algorithm-language candidate; implement only from the scrubbed Model A dossier and retained-set contract."
 "7707","cubing/twisty.js","https://github.com/cubing/twisty.js","HyperTwist","2.0","95.0","95.0","P0","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Moderate modification. Treat cubing/twisty.js as a family-level donor for HyperTwist: extract the implementation layer that matches its strongest domain contribution, preserve its protocols/data models/CLI or renderer boundaries, and adapt only the surface integration needed for HyperTwist rather than rewriting it wholesale.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Verify actual capability surface, identify reusable subsystems, confirm integration boundary, test output formats, and decide whether promotion or demotion is justified after source inspection.","P0 tier audit. Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","cubing/twisty.js may hide higher-value reusable components in its cubing family than its surface framing suggests; inspect internals before treating it as merely a category duplicate.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect cubing/twisty.js at source level. Extract actual implemented capabilities, hidden modules, extension hooks, data models, and output contracts. Re-rank only if source evidence materially changes its donor/foundation potential for HyperTwist.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use cubing/twisty.js as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use cubing/twisty.js as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use cubing/twisty.js as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Source audit may reveal reusable abstractions that travel beyond the initially assigned project, especially in control-plane, visualization, renderer, or document/AI pipeline layers.","Do not omit for licensing. Exclude only from current horizon if source audit shows it is purely documentation, governance, packaging noise, or a weak duplicate with no meaningful reusable subsystem.","Current placement is preliminary and based on family fit, repo naming, and source grouping rather than source code inspection.","medium","Pre-source-audit supplemental intake classification derived from source group, owner family, project fit, and project-wide design language.","medium","Will increase only after direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive; license is tracked separately only for optional review and packaging choices.","HyperTwist","HyperTwist","","Supplemental intake row added from v6 source-group expansion; reference docs may further inform merger logic.","cubing/twisty.js enters the corpus through the v6 supplemental intake. It is treated as donor bench for HyperTwist because its family suggests value in hypertwist family. likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or xr/game-engine surfaces relevant to hypercube and cube-training workflows. Its current judgment is intentionally provisional and should be upgraded or downgraded only after direct source inspection.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0003","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai; preliminary classification only.","HyperTwist","puzzle_simulation_training_donor","donor candidate","moderate modification","medium","cubing/twisty.js","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","","","","no","","","","v6_unified_source_of_truth","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references and prior Grok/initial-research docs used as reference, not as source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v6_supplemental_intake","New row created from supplemental intake (POO/Security/Agent/HyperTwist-ScriptoriumAI/Kali/Consider II).","2.0","1","95.0","Donor Bench","Focused restrictive clean-room donor target","","P2","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve viewer/player shell, scrubber, and twisty-element behavior through a scrubbed Model A handoff only.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — preferred path for reproducing compact twisty-viewer behavior in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only embedded viewer candidate; implement only from the scrubbed Model A dossier and retained-set contract."
 "7709","HactarCE/2x2x2x2-Scrambler","https://github.com/HactarCE/2x2x2x2-Scrambler","HyperTwist","2.0","63.0","63.0","P2","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Architecture only. Treat HactarCE/2x2x2x2-Scrambler as a design and subsystem reference first; source audit should look for transplantable patterns, adapters, data contracts, pipeline ideas, or UI/control abstractions before any decision to operationalize.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Verify actual capability surface, identify reusable subsystems, confirm integration boundary, test output formats, and decide whether promotion or demotion is justified after source inspection.","P2 tier audit. Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","HactarCE/2x2x2x2-Scrambler may hide higher-value reusable components in its HactarCE family than its surface framing suggests; inspect internals before treating it as merely a category duplicate.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect HactarCE/2x2x2x2-Scrambler at source level. Extract actual implemented capabilities, hidden modules, extension hooks, data models, and output contracts. Re-rank only if source evidence materially changes its donor/foundation potential for HyperTwist.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Source audit may reveal reusable abstractions that travel beyond the initially assigned project, especially in control-plane, visualization, renderer, or document/AI pipeline layers.","Do not omit for licensing. Exclude only from current horizon if source audit shows it is purely documentation, governance, packaging noise, or a weak duplicate with no meaningful reusable subsystem.","Current placement is preliminary and based on family fit, repo naming, and source grouping rather than source code inspection.","low-to-medium","Pre-source-audit supplemental intake classification derived from source group, owner family, project fit, and project-wide design language.","low-to-medium","Will increase only after direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive; license is tracked separately only for optional review and packaging choices.","HyperTwist","HyperTwist","","Supplemental intake row added from v6 source-group expansion; reference docs may further inform merger logic.","HactarCE/2x2x2x2-Scrambler enters the corpus through the v6 supplemental intake. It is treated as reserve bench for HyperTwist because its family suggests value in hypertwist family. likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or xr/game-engine surfaces relevant to hypercube and cube-training workflows. Its current judgment is intentionally provisional and should be upgraded or downgraded only after direct source inspection.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0004","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai; preliminary classification only.","HyperTwist","puzzle_simulation_training_donor","future candidate","architecture only","low-to-medium","hactarce/2x2x2x2-scrambler","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","","","","no","","","","v6_unified_source_of_truth","GPL-3.0","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references and prior Grok/initial-research docs used as reference, not as source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v6_supplemental_intake","New row created from supplemental intake (POO/Security/Agent/HyperTwist-ScriptoriumAI/Kali/Consider II).","2.0","3","63.0","Donor Bench","Focused restrictive clean-room donor target","","P2","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0 and the source explicitly notes a ported lineage from an earlier scrambler. Retain it only as a focused clean-room donor target and implement any valuable behavior through a scrubbed first-party specification.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — this is the preferred path for reproducing the 2x2x2x2 scrambler/state behaviors in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only Melinda 2x2x2x2 candidate; keep copied-port lineage explicit and implement only from the scrubbed Model A dossier."
 "7814","kkoomen/qbr","https://github.com/kkoomen/qbr","HyperTwist","2.0","158.0","191.0","A","vision / perception / AR","foundation engine","vision donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, ta...","Integrate as a computer vision / AR subsystem for HyperTwist. Preserve the strongest existing pieces — camera ingest, calibration, segmentation/detection, pose or facelet extraction, state normalization, solver bridge, replay overlay, AR anchors — and expose them behind a portfolio-stable interface. Wire first into vivaansinghvi07/rubix-cube-solver, then into cubing/cubing.js for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: vivaansinghvi07/rubix-cube-solver, cubing/cubing.js, HactarCE/Hyperspeedcube.","Repurpose here means: turn it into a perception microservice, cube-state API, replay generator, or AR overlay donor for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for calibration routines, detection heuristics/models, color/state normalization, replay serialization, solver bridges, camera abstraction layers, and debug visualizations.","Audit kkoomen/qbr as a vision / perception / AR candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts. Decide whether the best extraction path is direct and whether it belongs as foundation engine / vision donor. Test the three merger paths in order: 1) vivaansinghvi07/rubix-cube-solver [perception + replay donor]; 2) cubing/cubing.js [state/render backend]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, tao-yu/Alg-Trainer, kkoomen/qbr, vivaansinghvi07/rubix-cube-solver, NuiLab/code-vr, molgenis/Graph2VR (core permissive parts), brianpeiris/RiftSketch, aMonteSl/CodeXR.","kkoomen/qbr is treated as integrate for HyperTwist because visible metadata points to the computer vision / AR layer. Surface signal: kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, ta... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","5.0","HT_cube_vision","HT_cube_vision_0001","computer vision / AR","Integrate as a shared building block across at least two projects. Its memo and bookmark signals place it in the computer vision / AR layer.","multi-project","computer vision / AR","integrate","heavy modification","medium","kkoomen/qbr","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","2.0","5","158.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-B/O preserve sequence closed for the currently justified slice. Preserve as the landed first-party recognition calibration, ordered face-observation, and webcam-shell lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while keeping multilingual/font redistribution and broader solve-shell ownership deferred."
-"7818","coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","2.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","Repurpose selected subsystems rather than the whole product. Mine the repo for synthesis orchestration, multilingual and speaker handling, local service wrappers, voice-conversion paths, and model-registry/license handling; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: rhasspy/piper, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a bounded voice-service seam, coach narration donor, or multilingual TTS and cloning donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect public API, synthesis and orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, model registry files, and hidden experimental modules. Look for synthesis orchestration, sentence splitting, multilingual and speaker handling, voice conversion, server deployment patterns, and model-license metadata.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","coqui-ai/TTS is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: https://github.com/coqui-ai/TTS – Coqui XTTS v2. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0002","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is the richest current voice-output and coaching donor, but it should stay behind a bounded voice-service seam because code and model payload licensing diverge.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","coqui-ai/tts","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","2.0","5","71.0","","","","","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"7818","coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","2.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","Repurpose selected subsystems rather than the whole product. Mine the repo for synthesis orchestration, multilingual and speaker handling, local service wrappers, voice-conversion paths, and model-registry/license handling; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: rhasspy/piper, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a bounded voice-service seam, coach narration donor, or multilingual TTS and cloning donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect public API, synthesis and orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, model registry files, and hidden experimental modules. Look for synthesis orchestration, sentence splitting, multilingual and speaker handling, voice conversion, server deployment patterns, and model-license metadata.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","coqui-ai/TTS is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: https://github.com/coqui-ai/TTS – Coqui XTTS v2. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0002","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is the richest current voice-output and coaching donor, but it should stay behind a bounded voice-service seam because code and model payload licensing diverge.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","coqui-ai/tts","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","2.0","5","71.0","","","","","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 6R-H is closed. Preserve as the landed bounded advanced narration orchestration lane under the current MPL boundary doctrine; keep model, voice, and payload review separate from the code-license judgment."
 "8543","vivaansinghvi07/rubix-cube-solver","https://github.com/vivaansinghvi07/rubix-cube-solver","HyperTwist","3.0","158.0","191.0","A","vision / perception / AR","foundation engine","vision donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are mo...","Integrate as a computer vision / AR subsystem for HyperTwist. Preserve the strongest existing pieces — camera ingest, calibration, segmentation/detection, pose or facelet extraction, state normalization, solver bridge, replay overlay, AR anchors — and expose them behind a portfolio-stable interface. Wire first into kkoomen/qbr, then into cubing/cubing.js for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: kkoomen/qbr, cubing/cubing.js, HactarCE/Hyperspeedcube.","Repurpose here means: turn it into a perception microservice, cube-state API, replay generator, or AR overlay donor for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for calibration routines, detection heuristics/models, color/state normalization, replay serialization, solver bridges, camera abstraction layers, and debug visualizations.","Audit vivaansinghvi07/rubix-cube-solver as a vision / perception / AR candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts. Decide whether the best extraction path is direct and whether it belongs as foundation engine / vision donor. Test the three merger paths in order: 1) kkoomen/qbr [foundation + perception donor]; 2) cubing/cubing.js [state/render backend]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 10","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are more than accurate enough in 2026 for reliable small-square/facelet color recognition on a standard 3x3 (or larger) Rubik's Cube under normal lighting. Production examples prove it:Multiple MIT-licensed projects (qb","vivaansinghvi07/rubix-cube-solver is treated as integrate for HyperTwist because current dossier work keeps it in the committed vision path as the strongest reconstruction and replay-oriented companion to qbr rather than as a generic donor.","memo","False","True","0.0","10.0","HT_cube_vision","HT_cube_vision_0002","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","vivaansinghvi07/rubix-cube-solver","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","3.0","5","158.0","Locked Parallel Foundation","Parallel foundation and reconstruction companion donor","","","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy wording to dossier-backed parallel-foundation posture on 2026-04-25.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-C/P/Q preserve sequence closed for the currently justified slice. Preserve as the landed first-party committed-face reconstruction, browser/webcam shell, and bounded solve-explanation/recommendation lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while broad solver-backend ownership and bundled twistysim.min.js redistribution stay deferred."
-"8547","ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","3.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for native STT runtime seams, VAD, grammar-constrained decoding, segmented speech capture, and server-side deployment patterns; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: SYSTRAN/faster-whisper, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into an offline STT sidecar, grammar-constrained command surface, or native speech-input donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C/C++ API surface; VAD path; grammar-constrained decoding; server/runtime examples; model loading and portability seams; tests and benchmarks.","Inspect package manifests, README/docs, src/include tree, examples, tests, CI workflows, build configs, model tooling, and hidden experimental modules. Look for VAD, grammar support, streaming/segmentation, server boundaries, device/runtime abstraction, and performance shortcuts.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","ggml-org/whisper.cpp is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: 7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0003","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded speech-input donor and offline/native STT sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","ggml-org/whisper.cpp","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","3.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"8547","ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","3.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for native STT runtime seams, VAD, grammar-constrained decoding, segmented speech capture, and server-side deployment patterns; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: SYSTRAN/faster-whisper, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into an offline STT sidecar, grammar-constrained command surface, or native speech-input donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C/C++ API surface; VAD path; grammar-constrained decoding; server/runtime examples; model loading and portability seams; tests and benchmarks.","Inspect package manifests, README/docs, src/include tree, examples, tests, CI workflows, build configs, model tooling, and hidden experimental modules. Look for VAD, grammar support, streaming/segmentation, server boundaries, device/runtime abstraction, and performance shortcuts.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","ggml-org/whisper.cpp is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: 7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0003","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded speech-input donor and offline/native STT sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","ggml-org/whisper.cpp","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","3.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-E, Phase 6R-U, Phase 6R-V, and Phase 6R-W are closed. Preserve as the landed bounded native speech-session, microphone-shell, permission-readiness, and payload-custody lane; keep broader payload shipping and model-license review separate."
 "11766","tao-yu/Alg-Trainer","https://github.com/tao-yu/Alg-Trainer","HyperTwist","4.0","156.0","189.0","A","cubing trainer / solver / timing","foundation engine","training donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving cube-state, scramble, scheduling, or timer internals while adapting pedagogy, analytics, and UI.","Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg t...","Integrate as a cubing / algorithm training subsystem for HyperTwist. Preserve the strongest existing pieces — scramble generation, algorithm database, recognition/training loop, timing/statistics, virtual cube, smartcube hooks, spaced repetition — and expose them behind a portfolio-stable interface. Wire first into poliva/cubedex, then into Lykos/cube_trainer for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: poliva/cubedex, Lykos/cube_trainer, cubing/cubing.js.","Repurpose here means: turn it into a trainer engine, solver/timer backend, recognition drill module, or method-specific practice mode for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Extract cube-state model; Normalize trainer/case schema; Expose analytics and replay hooks","full subsystem extraction review","Hidden value often sits in cube-state representation, scramble generation, weighted drill scheduling, recognition datasets, replay/timer internals, and case database schemas.","Inspect cube-state model; scramble generator; trainer weighting/scheduling; case database and metadata; replay/timer model; import/export of alg sets; smartcube or sensor adapters.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for algorithm databases, spaced-repetition logic, scramble generation, timer/stat code, virtual cube components, smartcube adapters, and custom-trainer configuration support.","Audit tao-yu/Alg-Trainer as a cubing trainer / solver / timing candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect cube-state model; scramble generator; trainer weighting/scheduling; case database and metadata; replay/timer model; import/export of alg sets; smartcube or sensor adapters. Decide whether the best extraction path is direct and whether it belongs as foundation engine / training donor. Test the three merger paths in order: 1) poliva/cubedex [specialized training UX donor]; 2) Lykos/cube_trainer [sampling/analytics donor]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","poliva/cubedex","specialized training UX donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","Lykos/cube_trainer","sampling/analytics donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg trainer (extend to hyper commutators). | tao-yu/Alg-Trainer — MIT — Multi-set alg trainer.","tao-yu/Alg-Trainer is treated as integrate for HyperTwist because visible metadata points to the cubing / algorithm training layer. Surface signal: Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg t... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","5.0","HT_training_stack","HT_training_stack_0001","cubing / algorithm training","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the cubing / algorithm training layer.","HyperTwist","cubing / algorithm training","integrate","moderate modification","medium","tao-yu/alg-trainer","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","4.0","5","156.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 1R closed. Preserve as a landed first-party permissive lane; keep notices and attribution visible and widen only through ordinary owned enhancement work."
-"11769","rhasspy/piper","https://github.com/rhasspy/piper","multi-project","4.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","Repurpose selected subsystems rather than the whole product. Mine the repo for lean local TTS runtime, HTTP wrapping, voice loading and download logic, streaming WAV and raw output, and ONNX/eSpeak integration; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: coqui-ai/TTS, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a lean offline TTS sidecar or direct local narration donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C++ runtime core; voice loading and download path; streaming output; HTTP service boundary; speaker and phonemization config; selected voice artifact constraints.","Inspect package manifests, README/docs, src tree, examples, tests, build configs, voice catalog files, and hidden runtime switches. Look for ONNX runtime integration, phonemization, lightweight service boundaries, audio streaming, voice acquisition, and deployment constraints.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","rhasspy/piper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended). The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0004","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a lean local TTS sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","rhasspy/piper","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","4.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"11769","rhasspy/piper","https://github.com/rhasspy/piper","multi-project","4.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","Repurpose selected subsystems rather than the whole product. Mine the repo for lean local TTS runtime, HTTP wrapping, voice loading and download logic, streaming WAV and raw output, and ONNX/eSpeak integration; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: coqui-ai/TTS, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a lean offline TTS sidecar or direct local narration donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C++ runtime core; voice loading and download path; streaming output; HTTP service boundary; speaker and phonemization config; selected voice artifact constraints.","Inspect package manifests, README/docs, src tree, examples, tests, build configs, voice catalog files, and hidden runtime switches. Look for ONNX runtime integration, phonemization, lightweight service boundaries, audio streaming, voice acquisition, and deployment constraints.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","rhasspy/piper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended). The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0004","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a lean local TTS sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","rhasspy/piper","multi-project","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","4.0","5","71.0","","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-G is closed. Preserve as the landed bounded local narration sidecar lane; keep broad voice-model and payload review separate."
 "11771","cubing/cubing.js","https://github.com/cubing/cubing.js","HyperTwist","5.0","152.0","185.0","A","interface / visualization / shell surface","foundation engine","visualization donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving scene/layout primitives and replacing surrounding data models or backend assumptions.","Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above.","Integrate as a ui / design / frontend subsystem for HyperTwist. Preserve the strongest existing pieces — component library, canvas/animation engine, interaction patterns, layout/state models, accessibility hooks, theming, editor widgets — and expose them behind a portfolio-stable interface. Wire first into HactarCE/Hyperspeedcube, then into kkoomen/qbr for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: HactarCE/Hyperspeedcube, kkoomen/qbr.","Repurpose here means: turn it into a frontend interaction donor, canvas/editor pattern library, or polished shell layer on top of existing anchors.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Extract layout/scene primitives; Map import/export schema; Detach UI shell from backend assumptions","full subsystem extraction review","Hidden value often sits in scene graph/canvas model, component primitives, import/export schema, gesture/keyboard interactions, and plugin-ready layout abstractions.","Inspect scene graph/canvas data model; layout primitives; component library; keyboard/gesture interactions; import/export schema; theming; plugin or extension hooks.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for reusable canvas/editor components, design tokens, state models, keyboard shortcuts, drag/drop, accessibility, virtualization, and polished interaction patterns.","Audit cubing/cubing.js as a interface / visualization / shell surface candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect scene graph/canvas data model; layout primitives; component library; keyboard/gesture interactions; import/export schema; theming; plugin or extension hooks. Decide whether the best extraction path is direct and whether it belongs as foundation engine / visualization donor. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","medium-high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above.","cubing/cubing.js is treated as integrate for HyperTwist because visible metadata points to the ui / design / frontend layer. Surface signal: Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above. The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","1.0","HT_cube_semantics","HT_cube_semantics_0001","cubing / algorithm training","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the cubing / algorithm training layer.","HyperTwist","cubing / algorithm training","integrate","moderate modification","medium","cubing/cubing.js","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MPL-2.0 OR GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","Existing v5 row reaffirmed or widened by v6 supplemental intake.","5.0","5","152.0","Locked Strategic Donor","Boundary-sensitive classic-cubing semantics and rendering donor","","P1","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","The repo is dual-licensed MPL-2.0 OR GPL-3.0-or-later. HyperTwist can consume it as a package or bounded adapter under the MPL side, but should avoid a carefree deep private source fork of upstream files.","Prefer package/dependency consumption or a bounded adapter seam under the MPL side; avoid deep private forks of upstream source files.","Preserve MPL notices and publish modifications to MPL-covered files when distribution obligations apply; avoid assuming the GPL side is the intended operational path.","Only if you later need to replace narrow upstream-covered seams with first-party equivalents or avoid carrying MPL-governed source modifications.","high","dual-license-boundary-review","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 4R-A closed. Preserve as the landed first-party classic-cubing semantic/runtime adapter lane through explicit MPL-aware dependency or adapter use; keep notices and publication duties explicit before any future direct upstream file modification."
 "11848","cahidenes/rubiks-cube-solver","https://github.com/cahidenes/rubiks-cube-solver","HyperTwist","6.0","148.0","174.0","A","vision / perception / AR","foundation engine","vision donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","cahidenes/rubiks-cube-solver — MIT — opposite-corner capture heuristic, face-orientation fill logic, cube-string assembly from partial capture, and lightweight HSV or per-sticker bookkeeping retained only as a comparison adjunct behind qbr and rubix-cube-solver.","Do not integrate as a primary perception subsystem. Retain only as a narrow comparison adjunct behind the landed qbr calibration/webcam owner and landed rubix-cube-solver reconstruction/browser owner. Any later use should surface face-placement, cube-string assembly, or divergence-reporting comparisons without widening camera shell, primary calibration, or solver ownership.","Keep subordinate to qbr and rubix-cube-solver inside the current recognition stack. Use only for bounded cross-checking or disagreement reporting; do not form a separate recognition silo or reopen primary recognition ownership.","Repurpose here means: optional face-placement, cube-string, or disagreement comparator logic only, not a primary recognition service or AR shell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect opposite-corner capture assumptions; face-placement fill; cube-string assembly; per-sticker bookkeeping; HSV sampling; disagreement-reporting value; reasons it should stay subordinate to qbr and rubix-cube-solver.","Inspect face-orientation fill logic, cube-string assembly, per-sticker bookkeeping, HSV sampling, opposite-corner capture assumptions, and divergence-reporting possibilities against the landed first-party stack.","Audit cahidenes/rubiks-cube-solver only as a retained recognition-comparison adjunct for HyperTwist. Inspect opposite-corner capture assumptions, face-orientation fill logic, cube-string assembly from partial capture, and lightweight HSV or per-sticker bookkeeping. Do not reopen it as a primary recognition, calibration, or solver owner. Compare only against the landed qbr, rubix-cube-solver, and correction-stack seams and report whether any narrower divergence-reporting gap remains.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","cahidenes/rubiks-cube-solver — MIT — OpenCV cube detection + Kociemba solver. | cahidenes/rubiks-cube-solver — MIT — OpenCV + solver. | Yes — we already have several strong open-source visual models for cube recognition from earlier in our conversation. I went back through the entire history and pulled the exact ones we discussed (qbr, vivaansinghvi07/rubix-cube-solver, tentone/rubix-solver, cahidenes/rubiks-cube-solver). These are still the high","cahidenes/rubiks-cube-solver is treated as integrate for HyperTwist because the current dossier keeps it active as a strategic donor for constrained capture flow, stickerless-friendly grouping, face-placement logic, and solver-handoff normalization behind the two vision anchors.","memo","False","True","0.0","5.0","HT_cube_vision","HT_cube_vision_0003","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","cahidenes/rubiks-cube-solver","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","6.0","5","148.0","Locked Strategic Donor","Retained face-placement and cube-string comparison adjunct","","","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy candidate-core wording to dossier-backed strategic-donor posture on 2026-04-25.","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow face-placement, cube-string assembly, and optional two-opposite-corner comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
 "11852","tentone/rubix-solver","https://github.com/tentone/rubix-solver","HyperTwist","7.0","148.0","174.0","A","vision / perception / AR","foundation engine","vision donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","tentone/rubix-solver — README-only MIT posture — quad clustering, square-mask color sampling, center-color face identification, and lightweight native face or state comparison retained only as a comparison adjunct behind qbr and rubix-cube-solver.","Do not integrate as a primary perception subsystem. Retain only as a narrow native comparison adjunct behind the landed qbr calibration/webcam owner and landed rubix-cube-solver reconstruction/browser owner. Any later use should surface quad, mask, or divergence comparisons without widening the local camera shell or brute-force solve shell.","Keep subordinate to qbr and rubix-cube-solver inside the current recognition stack. Use only for bounded native comparison, side-by-side validation, or disagreement reporting; do not form a separate recognition silo or promote the local solve shell.","Repurpose here means: optional quad-sorting, square-mask, and native face-state comparator logic only, not a primary recognition service or solver shell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect quad clustering; sort stability; square-mask and threshold logic; center-color face labeling; native state-mutation semantics; disagreement-reporting value; reasons it should stay subordinate to qbr and rubix-cube-solver.","Inspect quad detection and sorting, square-mask color sampling, center-color face labeling, native face-array mutation behavior, and brute-force solve-shell exclusion.","Audit tentone/rubix-solver only as a retained native recognition-comparison adjunct for HyperTwist. Inspect quad clustering, square-mask color sampling, center-color face identification, native face-array mutation behavior, and brute-force solve-shell exclusion. Do not reopen it as a primary recognition, correction, or solver owner. Compare only against the landed qbr, rubix-cube-solver, and correction-stack seams and report whether any narrower divergence-reporting gap remains.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","memo mentions: 7","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","tentone/rubix-solver — MIT — OpenCV cube detection. | Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are more than accurate enough in 2026 for reliable small-square/facelet color recognition on a standard 3x3 (or larger) Rubik's Cube under normal lighting. Productio","tentone/rubix-solver is treated as integrate for HyperTwist because the current dossier keeps it active as a strategic donor for compact C++ and OpenCV detection heuristics and comparison-bench value, not for its brute-force solver shell.","memo","False","True","0.0","7.0","HT_cube_vision","HT_cube_vision_0004","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","tentone/rubix-solver","HyperTwist","","","","","","","","","","","","","","","","Original global operational v3 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","7.0","5","148.0","Locked Strategic Donor","Retained native quad and color comparison adjunct","","","permissive_or_noncopyleft_known","direct_incorporation_ok","Practical working assumption remains MIT from README and repo presentation, but the checked mirror lacks a bundled top-level license file. Keep this row permissive-active only as a subordinate recognition comparison adjunct, and capture the final authoritative upstream license text before any direct vendoring.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","medium","readme-only-license-capture-before-direct-vendoring","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy candidate-core wording to dossier-backed strategic-donor posture on 2026-04-25.","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow native quad-sorting, square-mask color sampling, center-color face labeling, and face-state comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
diff --git a/docs/repo_portfolio_unified_phase_g_v6_3.csv b/docs/repo_portfolio_unified_phase_g_v6_3.csv
index 2982cc1..e5a3e13 100644
--- a/docs/repo_portfolio_unified_phase_g_v6_3.csv
+++ b/docs/repo_portfolio_unified_phase_g_v6_3.csv
@@ -1,15 +1,15 @@
 "execution_queue_order","repo","primary_url","best_fit_project_v2","project_rank","portfolio_priority_score","execution_priority_score_v3","priority_band","taxonomy_hardened","archetype_primary","archetype_secondary","portfolio_role_v3","recommended_action_v2","repurposing_potential_v2","modification_scope_detail_v3","capability_extract","integration_realization_detail","consolidation_detail","repurpose_detail","realization_checklist","inspection_depth_recommendation","hidden_value_hypothesis","source_inspection_questions","source_code_audit_targets","coding_model_instruction_v3","merger_partner_1","merger_type_1","merger_rationale_1","merger_partner_2","merger_type_2","merger_rationale_2","merger_partner_3","merger_type_3","merger_rationale_3","cross_project_transfer_targets","cross_project_transfer_rationale","exclusion_discipline","exclusion_reasoning_v3","confidence_v3","confidence_rationale_v3","confidence_reassessed","confidence_rationale","licensing_filter_status","bookmark_project_sections","bookmark_heading_paths_top","bookmark_description_sample","memo_signal_sample","expanded_reasoning","source_attachments","in_bookmarks","in_memo","bookmark_occurrences","memo_mentions","cluster_tag","source_audit_packet_id","category_guess_v2","reasoning","best_fit_project","category_guess","recommended_action","repurposing_potential","confidence","canonical_repo_key","alias_group_size","phase_g_bucket","phase_g_bucket_rank","phase_g_bucket_reason","phase_g_project_stack_layer","phase_g_inclusion_status","phase_g_source_audit_priority","phase_g_master_list_rationale","phase_g_project_rank","phase_g_conf_rank","phase_g_action_rank","phase_g_global_order","phase_g_project_order","phase_g_anomaly_flag","_repo_norm","v5_primary_eval_project","v5_runtime_project","v5_scriptorium_override_status","v5_scriptorium_phase_g_bucket","v5_scriptorium_phase_g_stack_layer","v5_scriptorium_phase_g_source_audit_priority","v5_scriptorium_current_reality_status","v5_scriptorium_actual_role","v5_scriptorium_evidence_summary","v5_scriptorium_supersedes_prior_assessment","v5_source_of_truth","v6_license_annotation","v6_license_annotation_status","v6_license_annotation_source","v6_supplemental_intake_present","v6_supplemental_source_groups","v6_supplemental_source_sections","v6_supplemental_source_files","v6_reference_material_position","v6_kali_agent_access_relevance","v6_branch_seed_prompt_included","v6_branch_seed_scope","v6_intake_wave","v6_notes","v6_source_of_truth","project_rank_num","audit_rank_num","bucket_rank_num","priority_num","copyleft_relevance_v6_1","copyleft_strategy_v6_1","copyleft_rationale_v6_1","preferred_boundary_model_v6_1","open_compliance_if_used_as_is_v6_1","reverse_engineer_if_proprietary_core_needed_v6_1","copyleft_strategy_confidence_v6_1","copyleft_manual_review_trigger_v6_1","as_is_incorporation_sensible_v6_1","v6_2_sre_layer","v6_2_sre_stratum","v6_2_sre_role","v6_2_sre_family","v6_2_related_kali_package","v6_2_related_upstream_repo","v6_2_kali_package_suffices_for_tool_execution","v6_2_upstream_repo_preferred_for_deep_eval","v6_2_index_page_followup_useful","v6_2_index_page_followup_reason","v6_2_sre_notes","v6_2_dnspy_ilspy_relevance","v6_3_source_of_truth","v6_3_merge_note","v6_3_live_state_2026_05_11","v6_3_reset_lane_2026_05_11","v6_3_reset_next_step_2026_05_11"
 "1.0","HactarCE/Hyperspeedcube","https://github.com/HactarCE/Hyperspeedcube","HyperTwist","1.0","160.0","193.0","A","hypercubing / nD engine","foundation engine","simulation donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving generalized puzzle/state/render logic while building a new application shell around it.","HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Exten...","Integrate as a hypercubing / nD simulation subsystem for HyperTwist. Preserve the strongest existing pieces — nD state model, move notation, renderer, projection controls, solver/traversal logic, puzzle serialization, replay — and expose them behind a portfolio-stable interface. Wire first into cubing/cubing.js, then into tao-yu/Alg-Trainer for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: cubing/cubing.js, tao-yu/Alg-Trainer, poliva/cubedex.","Repurpose here means: turn it into a higher-dimensional renderer/simulator donor and shared interaction grammar for HyperTwist and long-horizon VectorShell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate puzzle/state core; Extract render/input abstractions; Document notation and save format","full subsystem extraction review","Hidden value is likely in generalized puzzle/state representations, higher-dimensional transforms, notation systems, save formats, puzzle generators, and rendering abstractions.","Inspect generalized puzzle/state model; notation parser; transform math; rendering abstraction; save/load format; puzzle generator; input mapping; performance optimizations.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for higher-dimensional state/notation representations, projection math, renderer abstractions, puzzle serialization, controls, and replay/training hooks.","Audit HactarCE/Hyperspeedcube as a hypercubing / nD engine candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect generalized puzzle/state model; notation parser; transform math; rendering abstraction; save/load format; puzzle generator; input mapping; performance optimizations. Decide whether the best extraction path is direct and whether it belongs as foundation engine / simulation donor. Test the three merger paths in order: 1) cubing/cubing.js [3D engine + notation/state donor]; 2) tao-yu/Alg-Trainer [training UX donor]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","cubing/cubing.js","3D engine + notation/state donor","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","tao-yu/Alg-Trainer","training UX donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 6","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Extended Comprehensive Open-Source Building Blocks (April 2026)Updated exhaustive scan — focused on high-value for your codebase (simulators, trainers, vision, hyper).MIT or Apache Licensed (Fully permissive — fork/abs","HactarCE/Hyperspeedcube is treated as integrate for HyperTwist because visible metadata points to the hypercubing / nD simulation layer. Surface signal: HactarCE/Hyperspeedcube — MIT OR Apache-2.0 — Modern 3D/4D puzzle simulator (thousands of puzzles). | This makes CubeForge the first true hypercubing training platform — solving every pain point while keeping the 3D core rock-solid.Exten... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","6.0","HT_hyper_engine","HT_hyper_engine_0001","hypercubing / nD simulation","Integrate as a shared building block across at least two projects. Its memo and bookmark signals place it in the hypercubing / nD simulation layer.","multi-project","hypercubing / nD simulation","integrate","direct","medium","hactarce/hyperspeedcube","1.0","Locked Foundation","1.0","Primary architectural anchor for HyperTwist; strongest current fit in corpus for the 'nD / hypercubing simulation substrate' role and should be source-audited before alternative bases.","nD / hypercubing simulation substrate","Included","P0","HactarCE/Hyperspeedcube is placed in Locked Foundation for HyperTwist because it best serves the 'nD / hypercubing simulation substrate' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","2.0","2.0","","hactarce/hyperspeedcube","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","Existing v5 row reaffirmed or widened by v6 supplemental intake.","v6_unified_source_of_truth_pack","1.0","1","1.0","160.0","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-A/K/L/M/N preserve sequence closed. Preserve as the landed first-party hyper puzzle catalog, notation, replay-log serialization, replay verification, stats-shape, solve-record, and puzzle-DSL authoring lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, not from the old candidate queue wording."
-"1746.0","SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","1.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving parsers/graph schema/indexing while swapping layout, storage, or UX layers.","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for VAD-aware segmentation, batch transcription, timestamps, hotword and prefix conditioning, and Python service ergonomics; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: ggml-org/whisper.cpp, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a Python STT service, timestamped speech pipeline, or batch transcription donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Normalize graph schema; Decouple parser/indexer from UI; Expose query and layout services","deep source audit","Hidden value may sit in AST/indexing pipelines, call/dependency graph schema, incremental refresh, layout heuristics, graph query APIs, and serialization formats that can be lifted into VectorShell.","Inspect transcription API and dataclasses; batched inference; VAD chunking; word timestamps; hotwords and prefix conditioning; service-layer boundaries; benchmark and test coverage.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, benchmark scripts, and hidden experimental modules. Look for VAD, batch inference, timestamps, hotwords, model/runtime constraints, and service-layer boundaries.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","SYSTRAN/faster-whisper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0001","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded Python STT donor and service-layer candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","systran/faster-whisper","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current Python STT donor and service-layer candidate in the voice stack.","Cross-project / future-adjacent","Included","P2","SYSTRAN/faster-whisper is placed in Donor Bench for multi-project because it provides the clearest Python STT service-layer path in the voice stack. Recommended action remains repurpose, but the real retained value is batch transcription, VAD-aware chunking, timestamps, and Python-side service integration rather than any code-intelligence or graph role.","4.0","2.0","2.0","60.0","2.0","","systran/faster-whisper","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","1.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"1746.0","SYSTRAN/faster-whisper","https://github.com/SYSTRAN/faster-whisper","multi-project","1.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving parsers/graph schema/indexing while swapping layout, storage, or UX layers.","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for VAD-aware segmentation, batch transcription, timestamps, hotword and prefix conditioning, and Python service ergonomics; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: ggml-org/whisper.cpp, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a Python STT service, timestamped speech pipeline, or batch transcription donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Normalize graph schema; Decouple parser/indexer from UI; Expose query and layout services","deep source audit","Hidden value may sit in AST/indexing pipelines, call/dependency graph schema, incremental refresh, layout heuristics, graph query APIs, and serialization formats that can be lifted into VectorShell.","Inspect transcription API and dataclasses; batched inference; VAD chunking; word timestamps; hotwords and prefix conditioning; service-layer boundaries; benchmark and test coverage.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, benchmark scripts, and hidden experimental modules. Look for VAD, batch inference, timestamps, hotwords, model/runtime constraints, and service-layer boundaries.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT.","SYSTRAN/faster-whisper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: huggingface/faster-whisper – https://github.com/SYSTRAN/faster-whisper – MIT – Fast Whisper STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0001","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded Python STT donor and service-layer candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","systran/faster-whisper","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current Python STT donor and service-layer candidate in the voice stack.","Cross-project / future-adjacent","Included","P2","SYSTRAN/faster-whisper is placed in Donor Bench for multi-project because it provides the clearest Python STT service-layer path in the voice stack. Recommended action remains repurpose, but the real retained value is batch transcription, VAD-aware chunking, timestamps, and Python-side service integration rather than any code-intelligence or graph role.","4.0","2.0","2.0","60.0","2.0","","systran/faster-whisper","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","1.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-F is closed. Preserve as the landed complementary Python transcription-orchestration lane above the existing speech session boundary; do not widen it into top-level provider/session ownership."
 "2.0","kkoomen/qbr","https://github.com/kkoomen/qbr","HyperTwist","2.0","158.0","191.0","A","vision / perception / AR","foundation engine","vision donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, ta...","Integrate as a computer vision / AR subsystem for HyperTwist. Preserve the strongest existing pieces — camera ingest, calibration, segmentation/detection, pose or facelet extraction, state normalization, solver bridge, replay overlay, AR anchors — and expose them behind a portfolio-stable interface. Wire first into vivaansinghvi07/rubix-cube-solver, then into cubing/cubing.js for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: vivaansinghvi07/rubix-cube-solver, cubing/cubing.js, HactarCE/Hyperspeedcube.","Repurpose here means: turn it into a perception microservice, cube-state API, replay generator, or AR overlay donor for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for calibration routines, detection heuristics/models, color/state normalization, replay serialization, solver bridges, camera abstraction layers, and debug visualizations.","Audit kkoomen/qbr as a vision / perception / AR candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts. Decide whether the best extraction path is direct and whether it belongs as foundation engine / vision donor. Test the three merger paths in order: 1) vivaansinghvi07/rubix-cube-solver [perception + replay donor]; 2) cubing/cubing.js [state/render backend]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, tao-yu/Alg-Trainer, kkoomen/qbr, vivaansinghvi07/rubix-cube-solver, NuiLab/code-vr, molgenis/Graph2VR (core permissive parts), brianpeiris/RiftSketch, aMonteSl/CodeXR.","kkoomen/qbr is treated as integrate for HyperTwist because visible metadata points to the computer vision / AR layer. Surface signal: kkoomen/qbr — MIT — Webcam-based 3x3 solver with accurate OpenCV color detection (perfect vision starter). | kkoomen/qbr — MIT — Webcam CV solver (base for 3D vision). | kkoomen/qbr — MIT — Webcam CV color detection. | poliva/cubedex, ta... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","5.0","HT_cube_vision","HT_cube_vision_0001","computer vision / AR","Integrate as a shared building block across at least two projects. Its memo and bookmark signals place it in the computer vision / AR layer.","multi-project","computer vision / AR","integrate","heavy modification","medium","kkoomen/qbr","1.0","Locked Foundation","1.0","Primary architectural anchor for HyperTwist; strongest current fit in corpus for the 'Live cube-recognition substrate' role and should be source-audited before alternative bases.","Live cube-recognition substrate","Included","P0","kkoomen/qbr is placed in Locked Foundation for HyperTwist because it best serves the 'Live cube-recognition substrate' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","7705.0","4.0","","kkoomen/qbr","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","2.0","1","1.0","158.0","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-B/O preserve sequence closed for the currently justified slice. Preserve as the landed first-party recognition calibration, ordered face-observation, and webcam-shell lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while keeping multilingual/font redistribution and broader solve-shell ownership deferred."
 "","cubing/alg.js","https://github.com/cubing/alg.js","HyperTwist","2.0","95.0","95.0","P0","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Moderate modification. Treat cubing/alg.js as a family-level donor for HyperTwist: extract the implementation layer that matches its strongest domain contribution, preserve its protocols/data models/CLI or renderer boundaries, and adapt only the surface integration needed for HyperTwist rather than rewriting it wholesale.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Validate actual implementation breadth and hidden donor subsystems before promotion.","P0 tier source audit.","cubing/alg.js may contain stronger reusable internals than its surface description suggests.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Audit cubing/alg.js directly in source. Preserve distinctions between foundation, donor, reserve, comparator, and exclusion.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use cubing/alg.js as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use cubing/alg.js as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use cubing/alg.js as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Many supplemental repos may reveal transferable abstractions after source inspection.","Exclude only after source audit proves weak or purely documentary value.","Supplemental intake row. Current exclusion posture is provisional.","medium","Supplemental intake heuristic.","medium","Pending direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive.","HyperTwist","HyperTwist","","Supplemental intake; reference docs advisory only.","cubing/alg.js is introduced in v6 so the portfolio reflects the complete repo picture before exclusions.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0002","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai.","HyperTwist","puzzle_simulation_training_donor","donor candidate","moderate modification","medium","cubing/alg.js","1.0","Donor Bench","4.0","Standalone GPL parser/AST package with focused semantics value best preserved through Model A / Model B separation.","Focused restrictive clean-room donor target","included_in_v6_supplemental_intake","P2","Keep as its own clean-room donor lane because it isolates parser, AST, traversal, validation, keyboard-move, and URL/interchange semantics.","2.0","2.0","2.0","7708.0","1.0","","cubing/alg.js","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","no","v6_unified_source_of_truth_pack","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","","2.0","1","4.0","95.0","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve the parser/AST/traversal semantics through a scrubbed Model A handoff only.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — preferred path for reproducing parser/AST/traversal semantics in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only algorithm-language candidate; implement only from the scrubbed Model A dossier and retained-set contract."
 "","cubing/twisty.js","https://github.com/cubing/twisty.js","HyperTwist","2.0","95.0","95.0","P0","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Moderate modification. Treat cubing/twisty.js as a family-level donor for HyperTwist: extract the implementation layer that matches its strongest domain contribution, preserve its protocols/data models/CLI or renderer boundaries, and adapt only the surface integration needed for HyperTwist rather than rewriting it wholesale.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Validate actual implementation breadth and hidden donor subsystems before promotion.","P0 tier source audit.","cubing/twisty.js may contain stronger reusable internals than its surface description suggests.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Audit cubing/twisty.js directly in source. Preserve distinctions between foundation, donor, reserve, comparator, and exclusion.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use cubing/twisty.js as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use cubing/twisty.js as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use cubing/twisty.js as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Many supplemental repos may reveal transferable abstractions after source inspection.","Exclude only after source audit proves weak or purely documentary value.","Supplemental intake row. Current exclusion posture is provisional.","medium","Supplemental intake heuristic.","medium","Pending direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive.","HyperTwist","HyperTwist","","Supplemental intake; reference docs advisory only.","cubing/twisty.js is introduced in v6 so the portfolio reflects the complete repo picture before exclusions.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0003","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai.","HyperTwist","puzzle_simulation_training_donor","donor candidate","moderate modification","medium","cubing/twisty.js","1.0","Donor Bench","4.0","Standalone GPL viewer/player shell with focused browser twisty behavior best preserved through Model A / Model B separation.","Focused restrictive clean-room donor target","included_in_v6_supplemental_intake","P2","Keep as its own clean-room donor lane because it isolates browser twisty-viewer/player shell behavior and control-bar semantics.","2.0","2.0","2.0","7710.0","3.0","","cubing/twisty.js","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","no","v6_unified_source_of_truth_pack","GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","","2.0","1","4.0","95.0","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0-or-later and should remain a focused clean-room donor lane rather than direct donor code. Preserve viewer/player shell, scrubber, and twisty-element behavior through a scrubbed Model A handoff only.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — preferred path for reproducing compact twisty-viewer behavior in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only embedded viewer candidate; implement only from the scrubbed Model A dossier and retained-set contract."
-"1747.0","coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","2.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","Repurpose selected subsystems rather than the whole product. Mine the repo for synthesis orchestration, multilingual and speaker handling, local service wrappers, voice-conversion paths, and model-registry/license handling; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: rhasspy/piper, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a bounded voice-service seam, coach narration donor, or multilingual TTS and cloning donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect public API, synthesis and orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, model registry files, and hidden experimental modules. Look for synthesis orchestration, sentence splitting, multilingual and speaker handling, voice conversion, server deployment patterns, and model-license metadata.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","coqui-ai/TTS is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: https://github.com/coqui-ai/TTS – Coqui XTTS v2. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0002","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is the richest current voice-output and coaching donor, but it should stay behind a bounded voice-service seam because code and model payload licensing diverge.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","coqui-ai/tts","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current voice and coaching donor, but boundary-sensitive because code and model payload licensing must be separated.","Cross-project / future-adjacent","Included","P2","coqui-ai/TTS is placed in Donor Bench for multi-project because it provides the richest current voice-output and coaching architecture in the stack. Recommended action remains repurpose, but the real retained value is synthesis orchestration, multilingual and speaker handling, XTTS/voice-conversion paths, and model-registry discipline rather than broad assistant scope.","4.0","2.0","2.0","7713.0","4.0","","coqui-ai/tts","multi-project","","","","","","","","","","Original global Phase G v4 retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","2.0","3","4.0","71.0","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"1747.0","coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","2.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","Repurpose selected subsystems rather than the whole product. Mine the repo for synthesis orchestration, multilingual and speaker handling, local service wrappers, voice-conversion paths, and model-registry/license handling; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: rhasspy/piper, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a bounded voice-service seam, coach narration donor, or multilingual TTS and cloning donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect public API, synthesis and orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, model registry files, and hidden experimental modules. Look for synthesis orchestration, sentence splitting, multilingual and speaker handling, voice conversion, server deployment patterns, and model-license metadata.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","https://github.com/coqui-ai/TTS – Coqui XTTS v2.","coqui-ai/TTS is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: https://github.com/coqui-ai/TTS – Coqui XTTS v2. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0002","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is the richest current voice-output and coaching donor, but it should stay behind a bounded voice-service seam because code and model payload licensing diverge.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","coqui-ai/tts","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current voice and coaching donor, but boundary-sensitive because code and model payload licensing must be separated.","Cross-project / future-adjacent","Included","P2","coqui-ai/TTS is placed in Donor Bench for multi-project because it provides the richest current voice-output and coaching architecture in the stack. Recommended action remains repurpose, but the real retained value is synthesis orchestration, multilingual and speaker handling, XTTS/voice-conversion paths, and model-registry discipline rather than broad assistant scope.","4.0","2.0","2.0","7713.0","4.0","","coqui-ai/tts","multi-project","","","","","","","","","","Original global Phase G v4 retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","2.0","3","4.0","71.0","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 6R-H is closed. Preserve as the landed bounded advanced narration orchestration lane under the current MPL boundary doctrine; keep model, voice, and payload review separate from the code-license judgment."
 "","HactarCE/2x2x2x2-Scrambler","https://github.com/HactarCE/2x2x2x2-Scrambler","HyperTwist","2.0","63.0","63.0","P2","puzzle_simulation_training_donor","puzzle_simulation_training_donor","geometry_renderer_or_binding","donor bench","repurpose","architecture only","Architecture only. Treat HactarCE/2x2x2x2-Scrambler as a design and subsystem reference first; source audit should look for transplantable patterns, adapters, data contracts, pipeline ideas, or UI/control abstractions before any decision to operationalize.","HyperTwist family. Likely value lies in puzzle logic, scrambler design, algorithm representations, n-dimensional geometry, rendering, bindings, or XR/game-engine surfaces relevant to hypercube and cube-training workflows.","Primary realization path: use as a simulation, training, renderer, binding, or integration donor inside HyperTwist's dual stack of physical-cube analysis and higher-dimensional virtual training.","Consolidate by HyperTwist layer: core puzzle logic, scramblers/algs, renderer/bindings, XR/game-engine surfaces, experiments/comparators.","Repurpose toward renderer abstractions, puzzle-logic libraries, solver bindings, training UIs, or XR/engine adapters.","Validate actual implementation breadth and hidden donor subsystems before promotion.","P2 tier source audit.","HactarCE/2x2x2x2-Scrambler may contain stronger reusable internals than its surface description suggests.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Inspect state representations, move/alg parsers, scrambler generators, geometry/math cores, renderer abstractions, engine bindings, and XR/web surfaces.","Audit HactarCE/2x2x2x2-Scrambler directly in source. Preserve distinctions between foundation, donor, reserve, comparator, and exclusion.","HactarCE/Hyperspeedcube","foundation repo + feature donor","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into HactarCE/Hyperspeedcube for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","cubing/cubing.js","engine repo + interface donor","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into cubing/cubing.js for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","kkoomen/qbr","feature extraction only","Use HactarCE/2x2x2x2-Scrambler as a HyperTwist donor into kkoomen/qbr for puzzle logic, bindings, renderer ideas, or training/runtime augmentation.","multi-project","Many supplemental repos may reveal transferable abstractions after source inspection.","Exclude only after source audit proves weak or purely documentary value.","Supplemental intake row. Current exclusion posture is provisional.","low-to-medium","Supplemental intake heuristic.","low-to-medium","Pending direct source inspection.","Licensing intentionally ignored as a decision filter per canonical directive.","HyperTwist","HyperTwist","","Supplemental intake; reference docs advisory only.","HactarCE/2x2x2x2-Scrambler is introduced in v6 so the portfolio reflects the complete repo picture before exclusions.","HyperTwist & ScriptoriumAI.txt","0","0","1.0","0.0","HT_cube_semantics","HT_cube_semantics_0004","puzzle_simulation_training_donor","Supplemental v6 intake from hypertwist_and_scriptoriumai.","HyperTwist","puzzle_simulation_training_donor","future candidate","architecture only","low-to-medium","hactarce/2x2x2x2-scrambler","1.0","Donor Bench","6.0","GPL scrambler with copied-port lineage notes; valuable only through restrictive clean-room extraction, not donor use.","Focused restrictive clean-room donor target","included_in_v6_supplemental_intake","P2","Keep as a narrow but real clean-room donor for Melinda 2x2x2x2 state encoding, handedness/parity repair, random-state generation, move-family representation, and flat debug/teaching views.","2.0","3.0","3.0","7714.0","5.0","","hactarce/2x2x2x2-scrambler","HyperTwist","supplemental_v6_not_runtime_anchored","","","","","","","","no","v6_unified_source_of_truth_pack","GPL-3.0","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","","2.0","3","6.0","63.0","mixed_or_boundary_sensitive_known","reverse_engineer_preferred","The repo is GPL-3.0 and the source explicitly notes a ported lineage from an earlier scrambler. Retain it only as a focused clean-room donor target and implement any valuable behavior through a scrubbed first-party specification.","Model A may inspect the restrictive source; Model B should implement only from a scrubbed first-party specification.","Direct incorporation would require GPL-compatible distribution/compliance and is not the planned HyperTwist path.","Yes — this is the preferred path for reproducing the 2x2x2x2 scrambler/state behaviors in first-party code.","high","gpl-clean-room-donor","no","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","selected_not_live_clean_room_candidate","phase0r_clean_room_eval_then_model_a_model_b","Phase 1R closed. Retain as a Phase 5R clean-room-only Melinda 2x2x2x2 candidate; keep copied-port lineage explicit and implement only from the scrubbed Model A dossier."
 "3.0","vivaansinghvi07/rubix-cube-solver","https://github.com/vivaansinghvi07/rubix-cube-solver","HyperTwist","3.0","158.0","191.0","A","vision / perception / AR","foundation engine","vision donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are mo...","Integrate as a computer vision / AR subsystem for HyperTwist. Preserve the strongest existing pieces — camera ingest, calibration, segmentation/detection, pose or facelet extraction, state normalization, solver bridge, replay overlay, AR anchors — and expose them behind a portfolio-stable interface. Wire first into kkoomen/qbr, then into cubing/cubing.js for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: kkoomen/qbr, cubing/cubing.js, HactarCE/Hyperspeedcube.","Repurpose here means: turn it into a perception microservice, cube-state API, replay generator, or AR overlay donor for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for calibration routines, detection heuristics/models, color/state normalization, replay serialization, solver bridges, camera abstraction layers, and debug visualizations.","Audit vivaansinghvi07/rubix-cube-solver as a vision / perception / AR candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect preprocessing/calibration; tracking stabilization; state reconstruction; replay/event model; camera abstraction; fallback heuristics; testing assets/videos; performance shortcuts. Decide whether the best extraction path is direct and whether it belongs as foundation engine / vision donor. Test the three merger paths in order: 1) kkoomen/qbr [foundation + perception donor]; 2) cubing/cubing.js [state/render backend]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 10","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are more than accurate enough in 2026 for reliable small-square/facelet color recognition on a standard 3x3 (or larger) Rubik's Cube under normal lighting. Production examples prove it:Multiple MIT-licensed projects (qb","vivaansinghvi07/rubix-cube-solver is treated as integrate for HyperTwist because current dossier work keeps it in the committed vision path as the strongest reconstruction and replay-oriented companion to qbr rather than as a generic donor.","memo","False","True","0.0","10.0","HT_cube_vision","HT_cube_vision_0002","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","vivaansinghvi07/rubix-cube-solver","1.0","Locked Parallel Foundation","2.0","Part of the irreducible core stack for HyperTwist; kept as the parallel foundation and strongest reconstruction companion to qbr.","Parallel foundation and reconstruction companion donor","Included","P0","vivaansinghvi07/rubix-cube-solver is placed in Locked Parallel Foundation for HyperTwist because it best serves the 'Parallel foundation and reconstruction companion donor' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","7819.0","110.0","","vivaansinghvi07/rubix-cube-solver","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","3.0","1","2.0","158.0","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy wording to dossier-backed parallel-foundation posture on 2026-04-25.","implemented_live_permissive","landed_permissive_preserve","Later Phase 6R-C/P/Q preserve sequence closed for the currently justified slice. Preserve as the landed first-party committed-face reconstruction, browser/webcam shell, and bounded solve-explanation/recommendation lane; start future widening from ROADMAP.md, FEATURE_REGISTRY.md, REPO_LICENSE_TRACKING.md, and the repo-specific landed Phase 6R packets, while broad solver-backend ownership and bundled twistysim.min.js redistribution stay deferred."
-"1748.0","ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","3.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for native STT runtime seams, VAD, grammar-constrained decoding, segmented speech capture, and server-side deployment patterns; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: SYSTRAN/faster-whisper, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into an offline STT sidecar, grammar-constrained command surface, or native speech-input donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C/C++ API surface; VAD path; grammar-constrained decoding; server/runtime examples; model loading and portability seams; tests and benchmarks.","Inspect package manifests, README/docs, src/include tree, examples, tests, CI workflows, build configs, model tooling, and hidden experimental modules. Look for VAD, grammar support, streaming/segmentation, server boundaries, device/runtime abstraction, and performance shortcuts.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","ggml-org/whisper.cpp is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: 7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0003","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded speech-input donor and offline/native STT sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","ggml-org/whisper.cpp","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current native/offline STT sidecar candidate in the voice stack.","Cross-project / future-adjacent","Included","P2","ggml-org/whisper.cpp is placed in Donor Bench for multi-project because it is the clearest native/offline STT anchor in the voice stack. Recommended action remains repurpose, but the real retained value is a bounded speech-input runtime, VAD, grammar-constrained decoding, and portable deployment rather than a full product shell.","4.0","2.0","2.0","7850.0","6.0","","ggml-org/whisper.cpp","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","3.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"1748.0","ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","3.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","Repurpose selected subsystems rather than the whole product. Mine the repo for native STT runtime seams, VAD, grammar-constrained decoding, segmented speech capture, and server-side deployment patterns; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: SYSTRAN/faster-whisper, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into an offline STT sidecar, grammar-constrained command surface, or native speech-input donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C/C++ API surface; VAD path; grammar-constrained decoding; server/runtime examples; model loading and portability seams; tests and benchmarks.","Inspect package manifests, README/docs, src/include tree, examples, tests, CI workflows, build configs, model tooling, and hidden experimental modules. Look for VAD, grammar support, streaming/segmentation, server boundaries, device/runtime abstraction, and performance shortcuts.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT.","ggml-org/whisper.cpp is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: 7. Speech / Voice Models (STT + TTS)ggml-org/whisper.cpp – https://github.com/ggml-org/whisper.cpp – MIT – Native C++ Whisper for STT. The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0003","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a bounded speech-input donor and offline/native STT sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","ggml-org/whisper.cpp","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current native/offline STT sidecar candidate in the voice stack.","Cross-project / future-adjacent","Included","P2","ggml-org/whisper.cpp is placed in Donor Bench for multi-project because it is the clearest native/offline STT anchor in the voice stack. Recommended action remains repurpose, but the real retained value is a bounded speech-input runtime, VAD, grammar-constrained decoding, and portable deployment rather than a full product shell.","4.0","2.0","2.0","7850.0","6.0","","ggml-org/whisper.cpp","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","3.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-E, Phase 6R-U, Phase 6R-V, and Phase 6R-W are closed. Preserve as the landed bounded native speech-session, microphone-shell, permission-readiness, and payload-custody lane; keep broader payload shipping and model-license review separate."
 "4.0","tao-yu/Alg-Trainer","https://github.com/tao-yu/Alg-Trainer","HyperTwist","4.0","156.0","189.0","A","cubing trainer / solver / timing","foundation engine","training donor","locked core candidate","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving cube-state, scramble, scheduling, or timer internals while adapting pedagogy, analytics, and UI.","Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg t...","Integrate as a cubing / algorithm training subsystem for HyperTwist. Preserve the strongest existing pieces — scramble generation, algorithm database, recognition/training loop, timing/statistics, virtual cube, smartcube hooks, spaced repetition — and expose them behind a portfolio-stable interface. Wire first into poliva/cubedex, then into Lykos/cube_trainer for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: poliva/cubedex, Lykos/cube_trainer, cubing/cubing.js.","Repurpose here means: turn it into a trainer engine, solver/timer backend, recognition drill module, or method-specific practice mode for HyperTwist.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Extract cube-state model; Normalize trainer/case schema; Expose analytics and replay hooks","full subsystem extraction review","Hidden value often sits in cube-state representation, scramble generation, weighted drill scheduling, recognition datasets, replay/timer internals, and case database schemas.","Inspect cube-state model; scramble generator; trainer weighting/scheduling; case database and metadata; replay/timer model; import/export of alg sets; smartcube or sensor adapters.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for algorithm databases, spaced-repetition logic, scramble generation, timer/stat code, virtual cube components, smartcube adapters, and custom-trainer configuration support.","Audit tao-yu/Alg-Trainer as a cubing trainer / solver / timing candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect cube-state model; scramble generator; trainer weighting/scheduling; case database and metadata; replay/timer model; import/export of alg sets; smartcube or sensor adapters. Decide whether the best extraction path is direct and whether it belongs as foundation engine / training donor. Test the three merger paths in order: 1) poliva/cubedex [specialized training UX donor]; 2) Lykos/cube_trainer [sampling/analytics donor]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","poliva/cubedex","specialized training UX donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","Lykos/cube_trainer","sampling/analytics donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","project-local first","Cross-project transfer is possible, but the value is clearest inside the assigned project until source audit exposes more reusable primitives.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg trainer (extend to hyper commutators). | tao-yu/Alg-Trainer — MIT — Multi-set alg trainer.","tao-yu/Alg-Trainer is treated as integrate for HyperTwist because visible metadata points to the cubing / algorithm training layer. Surface signal: Alg-Trainer (tao-yu/Alg-Trainer) | tao-yu/Alg-Trainer — MIT — Most powerful multi-set alg trainer (ZBLL, full custom sets, smartcube/virtual cube). Live: https://tao-yu.github.io/Alg-Trainer/. | tao-yu/Alg-Trainer — MIT — Multi-set alg t... The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","5.0","HT_training_stack","HT_training_stack_0001","cubing / algorithm training","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the cubing / algorithm training layer.","HyperTwist","cubing / algorithm training","integrate","moderate modification","medium","tao-yu/alg-trainer","1.0","Locked Parallel Foundation","2.0","Part of the irreducible core stack for HyperTwist; complements a primary foundation in the 'Training / timing layer' role and should be preserved in the committed build path.","Training / timing layer","Included","P0","tao-yu/Alg-Trainer is placed in Locked Parallel Foundation for HyperTwist because it best serves the 'Training / timing layer' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","8548.0","111.0","","tao-yu/alg-trainer","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4.0","1","2.0","156.0","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 1R closed. Preserve as a landed first-party permissive lane; keep notices and attribution visible and widen only through ordinary owned enhancement work."
-"1749.0","rhasspy/piper","https://github.com/rhasspy/piper","multi-project","4.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","Repurpose selected subsystems rather than the whole product. Mine the repo for lean local TTS runtime, HTTP wrapping, voice loading and download logic, streaming WAV and raw output, and ONNX/eSpeak integration; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: coqui-ai/TTS, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a lean offline TTS sidecar or direct local narration donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C++ runtime core; voice loading and download path; streaming output; HTTP service boundary; speaker and phonemization config; selected voice artifact constraints.","Inspect package manifests, README/docs, src tree, examples, tests, build configs, voice catalog files, and hidden runtime switches. Look for ONNX runtime integration, phonemization, lightweight service boundaries, audio streaming, voice acquisition, and deployment constraints.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","rhasspy/piper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended). The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0004","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a lean local TTS sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","rhasspy/piper","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; lean direct local TTS sidecar candidate.","Cross-project / future-adjacent","Included","P2","rhasspy/piper is placed in Donor Bench for multi-project because it provides the leanest current local TTS runtime seam in the voice stack. Recommended action remains repurpose, but the real retained value is ONNX and eSpeak runtime simplicity, streaming output, and deployable local HTTP wrapping rather than broad voice-platform scope.","4.0","2.0","2.0","8565.0","8.0","","rhasspy/piper","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+"1749.0","rhasspy/piper","https://github.com/rhasspy/piper","multi-project","4.0","71.0","80.0","C","voice / multimodal I/O","subsystem donor","multimodal donor","donor bench","repurpose","moderate modification","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","Repurpose selected subsystems rather than the whole product. Mine the repo for lean local TTS runtime, HTTP wrapping, voice loading and download logic, streaming WAV and raw output, and ONNX/eSpeak integration; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: coqui-ai/TTS, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a lean offline TTS sidecar or direct local narration donor.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Isolate streaming/audio pipeline; Normalize command schema; Add local/offline fallback layer","deep source audit","Hidden value often lives in streaming segmentation, VAD, device abstraction, latency mitigation, translation chains, and local/offline fallback paths.","Inspect C++ runtime core; voice loading and download path; streaming output; HTTP service boundary; speaker and phonemization config; selected voice artifact constraints.","Inspect package manifests, README/docs, src tree, examples, tests, build configs, voice catalog files, and hidden runtime switches. Look for ONNX runtime integration, phonemization, lightweight service boundaries, audio streaming, voice acquisition, and deployment constraints.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","exclude from core, keep as donor","Do not let it consume roadmap as a full product shell; mine reusable engines, adapters, schemas, UX patterns, or datasets.","medium-low","single-source signal; clear taxonomy; mostly donor/reference role","medium","memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended).","rhasspy/piper is treated as repurpose for multi-project because visible metadata points to the voice / speech / audio layer. Surface signal: rhasspy/piper – https://github.com/rhasspy/piper – MIT – Real-time TTS (recommended). The fit looks real, but more as a donor/augmenter than as a standalone foundation.","memo","False","True","0.0","1.0","MU_misc","MU_misc_0004","voice / speech / audio","Repurpose selectively for multi-project. Its source-backed role is a lean local TTS sidecar candidate, not an exclusion-only adjunct.","future/adjacent use","voice / speech / audio","repurpose","moderate modification","medium","rhasspy/piper","1.0","Donor Bench","4.0","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; lean direct local TTS sidecar candidate.","Cross-project / future-adjacent","Included","P2","rhasspy/piper is placed in Donor Bench for multi-project because it provides the leanest current local TTS runtime seam in the voice stack. Recommended action remains repurpose, but the real retained value is ONNX and eSpeak runtime simplicity, streaming output, and deployable local HTTP wrapping rather than broad voice-platform scope.","4.0","2.0","2.0","8565.0","8.0","","rhasspy/piper","multi-project","","","","","","","","","","Original global Phase G v4 retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4.0","3","4.0","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-G is closed. Preserve as the landed bounded local narration sidecar lane; keep broad voice-model and payload review separate."
 "5.0","cubing/cubing.js","https://github.com/cubing/cubing.js","HyperTwist","5.0","152.0","185.0","A","interface / visualization / shell surface","foundation engine","visualization donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving scene/layout primitives and replacing surrounding data models or backend assumptions.","Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above.","Integrate as a ui / design / frontend subsystem for HyperTwist. Preserve the strongest existing pieces — component library, canvas/animation engine, interaction patterns, layout/state models, accessibility hooks, theming, editor widgets — and expose them behind a portfolio-stable interface. Wire first into HactarCE/Hyperspeedcube, then into kkoomen/qbr for orchestration, visualization, or data exchange.","Consolidate under the Hyperspeedcube + cubing.js + qbr nucleus, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: HactarCE/Hyperspeedcube, kkoomen/qbr.","Repurpose here means: turn it into a frontend interaction donor, canvas/editor pattern library, or polished shell layer on top of existing anchors.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Extract layout/scene primitives; Map import/export schema; Detach UI shell from backend assumptions","full subsystem extraction review","Hidden value often sits in scene graph/canvas model, component primitives, import/export schema, gesture/keyboard interactions, and plugin-ready layout abstractions.","Inspect scene graph/canvas data model; layout primitives; component library; keyboard/gesture interactions; import/export schema; theming; plugin or extension hooks.","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, config files, migrations/schemas, and hidden feature flags or experimental modules. Look for reusable canvas/editor components, design tokens, state models, keyboard shortcuts, drag/drop, accessibility, virtualization, and polished interaction patterns.","Audit cubing/cubing.js as a interface / visualization / shell surface candidate for HyperTwist. Do not stop at README-level features. Inspect: Inspect scene graph/canvas data model; layout primitives; component library; keyboard/gesture interactions; import/export schema; theming; plugin or extension hooks. Decide whether the best extraction path is direct and whether it belongs as foundation engine / visualization donor. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, plugin hooks, render/state models, datasets/test fixtures, and any subsystem stronger than the visible product shell.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","medium-high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","portfolio anchor or repeatedly surfaced core candidate; memo mentions: 1","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above.","cubing/cubing.js is treated as integrate for HyperTwist because visible metadata points to the ui / design / frontend layer. Surface signal: Other Notable ResourcesCubing.js library (for building your own tools): https://github.com/cubing/cubing.js – Open-source core used in many trainers above. The likely value is substantial enough to preserve as a named subsystem rather than just mining isolated ideas.","memo","False","True","0.0","1.0","HT_cube_semantics","HT_cube_semantics_0001","cubing / algorithm training","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the cubing / algorithm training layer.","HyperTwist","cubing / algorithm training","integrate","moderate modification","medium","cubing/cubing.js","1.0","Locked Strategic Donor","1.0","Dual MPL/GPL classic-cubing anchor with strong donor value, but not a carefree private-source fork candidate.","Boundary-sensitive classic-cubing semantics and rendering donor","Included","P1","Keep as the canonical classic-cubing semantics and interop donor, but only through its practical MPL path or bounded adapter/package consumption.","2.0","4.0","3.0","11771.0","112.0","","cubing/cubing.js","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MPL-2.0 OR GPL-3.0-or-later","known_from_reference_material","uploaded_reference_docs","yes","hypertwist_and_scriptoriumai","HyperTwist","HyperTwist & ScriptoriumAI.txt","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","Existing v5 row reaffirmed or widened by v6 supplemental intake.","v6_unified_source_of_truth_pack","5.0","1","1.0","152.0","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","The repo is dual-licensed MPL-2.0 OR GPL-3.0-or-later. HyperTwist can consume it as a package or bounded adapter under the MPL side, but should avoid a carefree deep private source fork of upstream files.","Prefer package/dependency consumption or a bounded adapter seam under the MPL side; avoid deep private forks of upstream source files.","Preserve MPL notices and publish modifications to MPL-covered files when distribution obligations apply; avoid assuming the GPL side is the intended operational path.","Only if you later need to replace narrow upstream-covered seams with first-party equivalents or avoid carrying MPL-governed source modifications.","high","dual-license-boundary-review","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Assigned to HT_cube_semantics during cluster normalization on 2026-04-25.","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 4R-A closed. Preserve as the landed first-party classic-cubing semantic/runtime adapter lane through explicit MPL-aware dependency or adapter use; keep notices and publication duties explicit before any future direct upstream file modification."
 "6.0","cahidenes/rubiks-cube-solver","https://github.com/cahidenes/rubiks-cube-solver","HyperTwist","6.0","148.0","174.0","A","vision / perception / AR","foundation engine","vision donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","cahidenes/rubiks-cube-solver — MIT — opposite-corner capture heuristic, face-orientation fill logic, cube-string assembly from partial capture, and lightweight HSV or per-sticker bookkeeping retained only as a comparison adjunct behind qbr and rubix-cube-solver.","Do not integrate as a primary perception subsystem. Retain only as a narrow comparison adjunct behind the landed qbr calibration/webcam owner and landed rubix-cube-solver reconstruction/browser owner. Any later use should surface face-placement, cube-string assembly, or divergence-reporting comparisons without widening camera shell, primary calibration, or solver ownership.","Keep subordinate to qbr and rubix-cube-solver inside the current recognition stack. Use only for bounded cross-checking or disagreement reporting; do not form a separate recognition silo or reopen primary recognition ownership.","Repurpose here means: optional face-placement, cube-string, or disagreement comparator logic only, not a primary recognition service or AR shell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect opposite-corner capture assumptions; face-placement fill; cube-string assembly; per-sticker bookkeeping; HSV sampling; disagreement-reporting value; reasons it should stay subordinate to qbr and rubix-cube-solver.","Inspect face-orientation fill logic, cube-string assembly, per-sticker bookkeeping, HSV sampling, opposite-corner capture assumptions, and divergence-reporting possibilities against the landed first-party stack.","Audit cahidenes/rubiks-cube-solver only as a retained recognition-comparison adjunct for HyperTwist. Inspect opposite-corner capture assumptions, face-orientation fill logic, cube-string assembly from partial capture, and lightweight HSV or per-sticker bookkeeping. Do not reopen it as a primary recognition, calibration, or solver owner. Compare only against the landed qbr, rubix-cube-solver, and correction-stack seams and report whether any narrower divergence-reporting gap remains.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","memo mentions: 5","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","cahidenes/rubiks-cube-solver — MIT — OpenCV cube detection + Kociemba solver. | cahidenes/rubiks-cube-solver — MIT — OpenCV + solver. | Yes — we already have several strong open-source visual models for cube recognition from earlier in our conversation. I went back through the entire history and pulled the exact ones we discussed (qbr, vivaansinghvi07/rubix-cube-solver, tentone/rubix-solver, cahidenes/rubiks-cube-solver). These are still the high","cahidenes/rubiks-cube-solver is treated as integrate for HyperTwist because the current dossier keeps it active as a strategic donor for constrained capture flow, stickerless-friendly grouping, face-placement logic, and solver-handoff normalization behind the two vision anchors.","memo","False","True","0.0","5.0","HT_cube_vision","HT_cube_vision_0003","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","cahidenes/rubiks-cube-solver","1.0","Locked Strategic Donor","3.0","High-value active vision donor for recognition heuristics, solver-bridge normalization, and validation behind qbr and vivaansinghvi07; not a foundation anchor.","Retained face-placement and cube-string comparison adjunct","Included","P1","cahidenes/rubiks-cube-solver is placed in Locked Strategic Donor for HyperTwist because it best serves the 'Active recognition-heuristics and validation donor' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","11849.0","113.0","","cahidenes/rubiks-cube-solver","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","6.0","2","3.0","148.0","permissive_or_noncopyleft_known","direct_incorporation_ok","This repo is aligned with a direct integration path for HyperTwist because it is either already implemented, strategically central, or donor-grade without a visible copyleft constraint in the current materials. Deep incorporation is sensible if the source audit confirms architectural cleanliness.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","high","strategic-or-implemented-component","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy candidate-core wording to dossier-backed strategic-donor posture on 2026-04-25.","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow face-placement, cube-string assembly, and optional two-opposite-corner comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
 "7.0","tentone/rubix-solver","https://github.com/tentone/rubix-solver","HyperTwist","7.0","148.0","174.0","A","vision / perception / AR","foundation engine","vision donor","locked strategic donor","integrate","direct","Keep the core engine or major subsystem mostly intact; change wrappers, branding, storage/auth, and integration seams so it becomes a first-class part of the target stack. For this repo class, that usually means preserving calibration/detection/state-reconstruction logic while replacing camera UX and integration surfaces.","tentone/rubix-solver — README-only MIT posture — quad clustering, square-mask color sampling, center-color face identification, and lightweight native face or state comparison retained only as a comparison adjunct behind qbr and rubix-cube-solver.","Do not integrate as a primary perception subsystem. Retain only as a narrow native comparison adjunct behind the landed qbr calibration/webcam owner and landed rubix-cube-solver reconstruction/browser owner. Any later use should surface quad, mask, or divergence comparisons without widening the local camera shell or brute-force solve shell.","Keep subordinate to qbr and rubix-cube-solver inside the current recognition stack. Use only for bounded native comparison, side-by-side validation, or disagreement reporting; do not form a separate recognition silo or promote the local solve shell.","Repurpose here means: optional quad-sorting, square-mask, and native face-state comparator logic only, not a primary recognition service or solver shell.","Map module boundaries; Identify hidden reusable internals; Define adapter/API boundary to target anchors; Write extraction tests against upstream behavior; Benchmark calibration pipeline; Extract state reconstruction; Wrap with camera/AR adapter","full subsystem extraction review","Hidden value often sits in calibration, preprocessing, stabilization, object/state reconstruction, replay artifacts, and camera-to-domain state pipelines that are not obvious from demos.","Inspect quad clustering; sort stability; square-mask and threshold logic; center-color face labeling; native state-mutation semantics; disagreement-reporting value; reasons it should stay subordinate to qbr and rubix-cube-solver.","Inspect quad detection and sorting, square-mask color sampling, center-color face labeling, native face-array mutation behavior, and brute-force solve-shell exclusion.","Audit tentone/rubix-solver only as a retained native recognition-comparison adjunct for HyperTwist. Inspect quad clustering, square-mask color sampling, center-color face identification, native face-array mutation behavior, and brute-force solve-shell exclusion. Do not reopen it as a primary recognition, correction, or solver owner. Compare only against the landed qbr, rubix-cube-solver, and correction-stack seams and report whether any narrower divergence-reporting gap remains.","kkoomen/qbr","foundation + perception donor","Use this repo against the partner as an augmenting layer; preserve the partner as the likely base and mine this repo for capabilities that improve breadth, UX, or specialization.","vivaansinghvi07/rubix-cube-solver","perception + replay donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cubing/cubing.js","state/render backend","Use the partner for canonical state or rendering abstractions and merge this repo's specialized logic on top.","VectorShell | ScriptoriumAI","VectorShell can borrow spatial/rendering and perception primitives; ScriptoriumAI can borrow tutorial/educational visualization patterns rather than the full engine.","do not exclude","Keep in active merge-set and force full source audit before any demotion.","high","single-source signal; clear taxonomy; active integration value; foundation-level fit","high","memo mentions: 7","Licensing intentionally ignored as a decision filter in this canonical evaluation; assess only architecture, capability, donor value, and concept transfer.","","","","tentone/rubix-solver — MIT — OpenCV cube detection. | Yes — 100% possible to consolidate everything into one no-compromises, enterprise-grade platform. You're not half-arsing it, and neither am I. Modern vision models (and even classical OpenCV pipelines refined over the last decade) are more than accurate enough in 2026 for reliable small-square/facelet color recognition on a standard 3x3 (or larger) Rubik's Cube under normal lighting. Productio","tentone/rubix-solver is treated as integrate for HyperTwist because the current dossier keeps it active as a strategic donor for compact C++ and OpenCV detection heuristics and comparison-bench value, not for its brute-force solver shell.","memo","False","True","0.0","7.0","HT_cube_vision","HT_cube_vision_0004","computer vision / AR","Integrate primarily for HyperTwist. Its memo and bookmark signals place it in the computer vision / AR layer.","HyperTwist","computer vision / AR","integrate","heavy modification","medium","tentone/rubix-solver","1.0","Locked Strategic Donor","3.0","High-value active native-CV donor for detection heuristics and comparison benchmarking behind the vision anchors; not a foundation anchor.","Retained native quad and color comparison adjunct","Included","P1","tentone/rubix-solver is placed in Locked Strategic Donor for HyperTwist because it best serves the 'Active native-CV donor and comparison bench' role; recommended action is 'integrate' with repurposing scope 'direct'. Confidence is high because this remains a metadata-level judgment until source audit confirms hidden modules, plugin points, adapters, or architectural strengths.","2.0","5.0","3.0","11852.0","114.0","","tentone/rubix-solver","HyperTwist","","","","","","","","","","Original global Phase G v4 retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Usually indirect","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","7.0","2","3.0","148.0","permissive_or_noncopyleft_known","direct_incorporation_ok","Practical working assumption remains MIT from README and repo presentation, but the checked mirror lacks a bundled top-level license file. Keep this row permissive-active only as a subordinate recognition comparison adjunct, and capture the final authoritative upstream license text before any direct vendoring.","Direct embed, vendored module, package dependency, or tightly integrated adapter as the architecture requires.","Typically preserve notices, attribution, and license text where required; no special copyleft-driven disclosure posture is normally needed.","Usually unnecessary unless you later decide the existing implementation is too constraining architecturally.","medium","readme-only-license-capture-before-direct-vendoring","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Normalized legacy candidate-core wording to dossier-backed strategic-donor posture on 2026-04-25.","selected_not_live_permissive_candidate","phase2rc_recognition_adjunct_gap_eval_only","Phase 2R-C plus the 2026-05-27 recognition-comparison adjunct hierarchy clarification are closed. Retain only as a narrow native quad-sorting, square-mask color sampling, center-color face labeling, and face-state comparison adjunct beneath the landed qbr, rubix-cube-solver, and correction-stack owners; no default widening packet is open."
diff --git a/docs/repo_portfolio_unified_source_audit_v6_3.csv b/docs/repo_portfolio_unified_source_audit_v6_3.csv
index e24ffb8..14ae0da 100644
--- a/docs/repo_portfolio_unified_source_audit_v6_3.csv
+++ b/docs/repo_portfolio_unified_source_audit_v6_3.csv
@@ -247,7 +247,7 @@
 6) Best merge partners and exact coupling seam
 7) Reasons to promote / retain / demote
 8) Confidence change after source audit
-9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","SYSTRAN/faster-whisper is placed in Donor Bench for multi-project because it provides the clearest Python STT service-layer path in the voice stack. Recommended action remains repurpose, but the real retained value is batch transcription, VAD-aware chunking, timestamps, and Python-side service integration rather than any code-intelligence or graph role.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current Python STT donor and service-layer candidate in the voice stack.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","MU_misc_0001","MU_misc","systran/faster-whisper","","","","","","","Original global P0-P3 source audit retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","SYSTRAN/faster-whisper is placed in Donor Bench for multi-project because it provides the clearest Python STT service-layer path in the voice stack. Recommended action remains repurpose, but the real retained value is batch transcription, VAD-aware chunking, timestamps, and Python-side service integration rather than any code-intelligence or graph role.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current Python STT donor and service-layer candidate in the voice stack.","Audit SYSTRAN/faster-whisper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: transcription API and dataclasses, batched inference, VAD chunking, word timestamps, hotwords and prefix conditioning, service-layer boundaries, and benchmark/test coverage. Decide whether it should remain the primary Python STT donor and what should stay behind a bounded Python service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","MU_misc_0001","MU_misc","systran/faster-whisper","","","","","","","Original global P0-P3 source audit retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded Python STT donor/service candidate; review chosen model checkpoints separately, but no clean-room path is required by default.","Use directly as a bounded Python STT service or adapter layer; keep model/runtime selection and deployment behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model checkpoints separately from the code license.","Usually unnecessary unless you later decide to replace a narrow hot path or remove Python/CTranslate2 dependencies.","high","model-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-F is closed. Preserve as the landed complementary Python transcription-orchestration lane above the existing speech session boundary; do not widen it into top-level provider/session ownership."
 "coqui-ai/TTS","https://github.com/coqui-ai/TTS","multi-project","Donor Bench","Cross-project / future-adjacent","P2","8352","10346","49.0","71.0","80.0","medium","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","repurpose","moderate modification","Determine whether coqui-ai/TTS should stay donor/merge-tier for multi-project, be promoted, or be demoted; identify concrete salvageable modules and best merge path.","API surface, synthesis orchestration, multilingual and speaker handling, voice conversion, model registry, deployment boundary, hidden modules","Inspect package manifests, README/docs, src tree, examples, tests, CI workflows, model registry files, and hidden experimental modules. Look for synthesis orchestration, sentence splitting, multilingual and speaker handling, voice conversion, server deployment patterns, and model-license metadata.","Inspect public API, synthesis and orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion.","Repurpose selected subsystems rather than the whole product. Mine the repo for synthesis orchestration, multilingual and speaker handling, local service wrappers, voice-conversion paths, and model-registry/license handling; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: rhasspy/piper, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a bounded voice-service seam, coach narration donor, or multilingual TTS and cloning donor.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","Upgrade if source reveals clean modular architecture, reusable core abstractions, strong adapters/plugins, robust tests, and direct fit to a locked stack.","Demote if reusable value is mostly superficial, undocumented complexity overwhelms salvage value, or higher-ranked repos clearly dominate the same role.","Capability inventory + salvage targets + promotion/demotion verdict","1) Confirmed visible capabilities
 2) Hidden capabilities found only in source
 3) Best salvageable modules/files/packages
@@ -256,7 +256,7 @@
 6) Best merge partners and exact coupling seam
 7) Reasons to promote / retain / demote
 8) Confidence change after source audit
-9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","coqui-ai/TTS is placed in Donor Bench for multi-project because it provides the richest current voice-output and coaching architecture in the stack. Recommended action remains repurpose, but the real retained value is synthesis orchestration, multilingual and speaker handling, XTTS/voice-conversion paths, and model-registry discipline rather than broad assistant scope.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current voice and coaching donor, but boundary-sensitive because code and model payload licensing must be separated.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","MU_misc_0002","MU_misc","coqui-ai/tts","","","","","","","Original global P0-P3 source audit retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","coqui-ai/TTS is placed in Donor Bench for multi-project because it provides the richest current voice-output and coaching architecture in the stack. Recommended action remains repurpose, but the real retained value is synthesis orchestration, multilingual and speaker handling, XTTS/voice-conversion paths, and model-registry discipline rather than broad assistant scope.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current voice and coaching donor, but boundary-sensitive because code and model payload licensing must be separated.","Audit coqui-ai/TTS as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: public API surface, synthesis/orchestration spine, server boundary, multilingual and speaker handling, XTTS path, model registry and license metadata, and optional voice conversion. Decide whether it should remain the strongest voice/coaching donor and what should stay behind a bounded voice-service seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","MU_misc_0002","MU_misc","coqui-ai/tts","","","","","","","Original global P0-P3 source audit retained","MPL-2.0 code; mixed model payload licenses","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","mixed_or_boundary_sensitive_known","bounded_sidecar_or_selective_reimplementation","Code is usable under MPL-2.0, but selected model weights carry mixed per-model licenses and some require separate terms. Keep the repo behind a bounded voice-service seam and decide model adoption case by case rather than treating it as a blanket permissive dependency.","Use the code behind a bounded voice-service seam; select model weights individually and keep model-license decisions separate from code adoption.","Preserve MPL notices and file-level obligations where applicable, and review each chosen model license or ToS separately before shipping.","Sometimes useful only if you later need a fully proprietary embedded voice stack or want to avoid model-license entanglement; not the default path.","medium","model-license-selection-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_boundary_sensitive","landed_boundary_sensitive_preserve","Phase 6R-H is closed. Preserve as the landed bounded advanced narration orchestration lane under the current MPL boundary doctrine; keep model, voice, and payload review separate from the code-license judgment."
 "ggml-org/whisper.cpp","https://github.com/ggml-org/whisper.cpp","multi-project","Donor Bench","Cross-project / future-adjacent","P2","8353","10347","49.0","71.0","80.0","medium","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","repurpose","moderate modification","Determine whether ggml-org/whisper.cpp should stay donor/merge-tier for multi-project, be promoted, or be demoted; identify concrete salvageable modules and best merge path.","native STT runtime seam, VAD, grammar-constrained decoding, server boundary, portability, hidden modules","Inspect package manifests, README/docs, src/include tree, examples, tests, CI workflows, build configs, model tooling, and hidden experimental modules. Look for VAD, grammar support, streaming/segmentation, server boundaries, device/runtime abstraction, and performance shortcuts.","Inspect C/C++ API surface; VAD path; grammar-constrained decoding; server/runtime examples; model loading and portability seams; tests and benchmarks.","Repurpose selected subsystems rather than the whole product. Mine the repo for native STT runtime seams, VAD, grammar-constrained decoding, segmented speech capture, and server-side deployment patterns; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: SYSTRAN/faster-whisper, rhasspy/piper, coqui-ai/TTS.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into an offline STT sidecar, grammar-constrained command surface, or native speech-input donor.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","Upgrade if source reveals clean modular architecture, reusable core abstractions, strong adapters/plugins, robust tests, and direct fit to a locked stack.","Demote if reusable value is mostly superficial, undocumented complexity overwhelms salvage value, or higher-ranked repos clearly dominate the same role.","Capability inventory + salvage targets + promotion/demotion verdict","1) Confirmed visible capabilities
 2) Hidden capabilities found only in source
 3) Best salvageable modules/files/packages
@@ -265,7 +265,7 @@
 6) Best merge partners and exact coupling seam
 7) Reasons to promote / retain / demote
 8) Confidence change after source audit
-9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","ggml-org/whisper.cpp is placed in Donor Bench for multi-project because it is the clearest native/offline STT anchor in the voice stack. Recommended action remains repurpose, but the real retained value is a bounded speech-input runtime, VAD, grammar-constrained decoding, and portable deployment rather than a full product shell.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current native/offline STT sidecar candidate in the voice stack.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","MU_misc_0003","MU_misc","ggml-org/whisper.cpp","","","","","","","Original global P0-P3 source audit retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","ggml-org/whisper.cpp is placed in Donor Bench for multi-project because it is the clearest native/offline STT anchor in the voice stack. Recommended action remains repurpose, but the real retained value is a bounded speech-input runtime, VAD, grammar-constrained decoding, and portable deployment rather than a full product shell.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; strongest current native/offline STT sidecar candidate in the voice stack.","Audit ggml-org/whisper.cpp as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C/C++ API surface, VAD, grammar-constrained decoding, server/runtime examples, model loading and portability seams, and benchmark/test coverage. Decide whether it should remain the primary offline STT sidecar candidate and what should stay behind a bounded native seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/benchmarks, and any subsystem stronger than the visible shell.","MU_misc_0003","MU_misc","ggml-org/whisper.cpp","","","","","","","Original global P0-P3 source audit retained","MIT","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. This repo is best used as a bounded offline STT sidecar or native speech-input seam; no clean-room path is required by default.","Use directly as a bounded native STT sidecar or library adapter; keep grammar, VAD, and model/runtime choices behind a speech-input seam.","Typically preserve notices, attribution, and license text where required; review selected model files or distributions separately from the code license.","Usually unnecessary unless you later choose to replace a narrow hot path or fully internalize the runtime.","high","model-artifact-review-recommended","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-E, Phase 6R-U, Phase 6R-V, and Phase 6R-W are closed. Preserve as the landed bounded native speech-session, microphone-shell, permission-readiness, and payload-custody lane; keep broader payload shipping and model-license review separate."
 "rhasspy/piper","https://github.com/rhasspy/piper","multi-project","Donor Bench","Cross-project / future-adjacent","P2","8354","10348","49.0","71.0","80.0","medium","Retain the valuable internal engine, but expect to replace UI/product shell, adapt schemas/APIs, and refactor boundaries so it can plug into the target anchors cleanly. For this repo class, that usually means preserving streaming/audio pipeline logic while adapting commands, wake flows, and assistant integration.","repurpose","moderate modification","Determine whether rhasspy/piper should stay donor/merge-tier for multi-project, be promoted, or be demoted; identify concrete salvageable modules and best merge path.","local TTS runtime seam, ONNX and eSpeak integration, streaming output, HTTP service boundary, voice catalog, hidden modules","Inspect package manifests, README/docs, src tree, examples, tests, build configs, voice catalog files, and hidden runtime switches. Look for ONNX runtime integration, phonemization, lightweight service boundaries, audio streaming, voice acquisition, and deployment constraints.","Inspect C++ runtime core; voice loading and download path; streaming output; HTTP service boundary; speaker and phonemization config; selected voice artifact constraints.","Repurpose selected subsystems rather than the whole product. Mine the repo for lean local TTS runtime, HTTP wrapping, voice loading and download logic, streaming WAV and raw output, and ONNX/eSpeak integration; keep what materially shortens build time, but rebind data contracts, permissions, storage, and deployment to the target architecture. Best first pairing order: coqui-ai/TTS, ggml-org/whisper.cpp, SYSTRAN/faster-whisper.","Consolidate under the project-specific anchor stack, not beside it as a separate silo. Normalize data contracts, auth/permissions, storage, and telemetry; then attach as a service, plugin, canvas layer, trainer, or analysis module. Descending combination order: sentrux/sentrux, HactarCE/Hyperspeedcube, outline/outline.","Repurpose here means: turn it into a lean offline TTS sidecar or direct local narration donor.","project-local anchor","base + donor","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","shared portfolio utility","augmenter","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","cross-project transfer candidate","future merger","Treat this pairing as a candidate donor merge rather than a replacement decision; inspect module boundaries to determine which side owns the final shell.","VectorShell | HyperTwist | ScriptoriumAI","This capability is broadly portable across the portfolio because interaction, orchestration, and shell/UI patterns can be shared with thin domain adapters.","Upgrade if source reveals clean modular architecture, reusable core abstractions, strong adapters/plugins, robust tests, and direct fit to a locked stack.","Demote if reusable value is mostly superficial, undocumented complexity overwhelms salvage value, or higher-ranked repos clearly dominate the same role.","Capability inventory + salvage targets + promotion/demotion verdict","1) Confirmed visible capabilities
 2) Hidden capabilities found only in source
 3) Best salvageable modules/files/packages
@@ -274,7 +274,7 @@
 6) Best merge partners and exact coupling seam
 7) Reasons to promote / retain / demote
 8) Confidence change after source audit
-9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","rhasspy/piper is placed in Donor Bench for multi-project because it provides the leanest current local TTS runtime seam in the voice stack. Recommended action remains repurpose, but the real retained value is ONNX and eSpeak runtime simplicity, streaming output, and deployable local HTTP wrapping rather than broad voice-platform scope.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; lean direct local TTS sidecar candidate.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","MU_misc_0004","MU_misc","rhasspy/piper","","","","","","","Original global P0-P3 source audit retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","","",""
+9) Open questions / blockers","Primary: Phase G v4 board (canonical ranking and current bucket); Operational v3 board filtered to this repo; Merger matrix rows involving this repo; VS Code packet row for this repo, if present. Optional: Cluster narratives row for matching cluster; Bookmark occurrence rows for this repo. Do not attach v1/v2 or preliminary memo unless a judgment conflict, lineage ambiguity, or rationale gap needs arbitration.","rhasspy/piper is placed in Donor Bench for multi-project because it provides the leanest current local TTS runtime seam in the voice stack. Recommended action remains repurpose, but the real retained value is ONNX and eSpeak runtime simplicity, streaming output, and deployable local HTTP wrapping rather than broad voice-platform scope.","Useful subsystem donor for multi-project, primarily in the 'Cross-project / future-adjacent' role; lean direct local TTS sidecar candidate.","Audit rhasspy/piper as a voice / multimodal I/O candidate for multi-project. Do not stop at README-level features. Inspect: C++ runtime core, voice loading and download path, streaming output, HTTP service boundary, speaker and phonemization config, and selected voice artifact constraints. Decide whether it should remain the lean direct local TTS sidecar candidate and what should stay behind a bounded local voice seam. Test the three merger paths in order: 1) project-local anchor [base + donor]; 2) shared portfolio utility [augmenter]; 3) cross-project transfer candidate [future merger]. Return hidden modules, reusable schemas, protocol layers, runtime boundaries, tests/fixtures, and any subsystem stronger than the visible shell.","MU_misc_0004","MU_misc","rhasspy/piper","","","","","","","Original global P0-P3 source audit retained","MIT code; voice artifacts reviewed separately","known_from_reference_material","uploaded_reference_docs","no","","","","Supplemental intake references are advisory only; v6 adjudication remains the source of truth.","Potentially relevant","yes","v6 unified all-project source-of-truth pack","v5_carry_forward","","v6_unified_source_of_truth_pack","4","3","71.0","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. The real review point is selected voice artifacts, not the runtime code; keep voice selection separate from code adoption.","Use directly as a bounded local TTS sidecar or simple HTTP service; keep selected voice artifacts under separate review.","Typically preserve notices, attribution, and license text where required; review chosen voices or model cards separately from the code license.","Usually unnecessary unless you later replace the runtime for packaging or architecture reasons.","high","voice-artifact-review-required","yes","","","","","","","","","","","","","v6.3_final_source_of_truth","Merged v6.1 copyleft layer and v6.2 SRE layer; use v6.3 docs + workbook as canonical handoff.","implemented_live_permissive","landed_permissive_preserve","Phase 6R-G is closed. Preserve as the landed bounded local narration sidecar lane; keep broad voice-model and payload review separate."
 "met4citizen/TalkingHead","https://github.com/met4citizen/TalkingHead","HyperTwist","Donor Bench","Browser embodied coach surface","P2","","","","","","medium","Retain the reusable avatar, lip-sync, and retargeting layers, but replace the demo shell, asset assumptions, and voice-service integration with HyperTwist-owned surfaces.","repurpose","moderate modification","Determine whether TalkingHead should remain the primary embodied coach donor and which runtime seams should stay bounded.","avatar runtime, lip-sync queueing, streamed speech, subtitle timing, avatarOnly embedding, retargeting","Inspect modules, examples, tests, site config, streaming demos, retargeter, and playback worklet code. Look for embodied-coach embedding, lip-sync, subtitle timing, gesture and expression surfaces, and asset assumptions.","Inspect talkinghead runtime; speech queueing and streaming; viseme and blendshape flow; avatarOnly embedding; retargeting; and audio worklet behavior.","Repurpose selected subsystems rather than the whole product. Mine the repo for embodied coach avatar runtime, lip-sync and subtitle timing, avatar-only embedding, retargeting, and streamed speech playback; keep what shortens build time, but rebind assets, voice services, and UI shell to the HyperTwist architecture.","","Repurpose here means: turn it into a browser-side embodied coach or companion layer.","","","","","","","","","","","","Upgrade if source reveals unusually reusable browser-runtime seams, clean adapter boundaries, robust tests, and direct leverage for HyperTwist companion surfaces.","Demote if retained value is mostly commodity glue, tightly bound to upstream shell assumptions, or clearly dominated by stronger neighboring repos in the same lane.","Capability inventory + salvage targets + donor-tier verdict","1) Confirmed source-backed browser/XR capabilities; 2) Best retained modules; 3) Boundaries to keep outside HyperTwist core; 4) Recommended pairing or dependency posture; 5) Risks or licensing notes.","Primary: HT_browser_surface cluster packet plus the repo-specific Markdown dossier. Optional: companion browser/XR rows in the same cluster.","met4citizen/TalkingHead is placed in Donor Bench for HyperTwist because it provides embodied coach UI, streaming lip-sync, retargeting, and avatar-only embedding behavior. Recommended action remains repurpose, but the retained value is a bounded browser coach surface rather than a general avatar product.","Useful subsystem donor for HyperTwist, primarily in the browser companion layer; strongest embodied coach and avatar presentation donor in the current stack.","Audit met4citizen/TalkingHead as a browser embodied-coach candidate for HyperTwist. Inspect the avatar runtime, streaming lip-sync, subtitle timing, avatarOnly embedding, retargeting, and audio worklet behavior. Decide which seams can be used directly and which must remain bounded behind the HyperTwist coaching shell.","HT_browser_surface_0001","HT_browser_surface","met4citizen/talkinghead","","","","","","","","MIT","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","v6_unified_source_of_truth_pack","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is MIT and direct use is allowed. Treat it as a bounded browser-side donor for embodied coach presentation rather than as a product shell.","Use directly as a bounded browser-side dependency or adapter layer; keep voice services, product logic, and asset provenance outside the upstream shell.","Typically preserve notices, attribution, and license text where required; review sample avatars or media separately from the code license.","Usually unnecessary unless you later replace a narrow upstream layer for product-shaping reasons.","high","standard-notice-review","yes","","","","","","","","","","","","","v6.3_markdown_backfill","Backfilled from canonized Markdown dossier pass on 2026-04-24.","implemented_live_permissive","landed_permissive_preserve","Phase 3R-E closed. Preserve as the landed first-party embodied companion, narration, lip-sync, gesture, subtitle, and avatar-embed lane; start future widening from the live-lane audit, then the 3R-E implementation packet, then REPO_LICENSE_TRACKING.md."
 "apache/echarts","https://github.com/apache/echarts","HyperTwist","Donor Bench","Browser analytics and reporting surface","P2","","","","","","medium","Retain the reusable chart runtime and reporting patterns, but keep HyperTwist domain schemas, page shell, and training logic outside the upstream system.","repurpose","moderate modification","Determine whether echarts should remain the primary browser analytics and reporting donor for HyperTwist companion surfaces.","option manager, datastore, zoom and history, thumbnail and export, SSR, chart modularity","Inspect manifests, README/docs, src core, model, data, component, export, and SSR folders. Look for modular chart runtime, data-store abstractions, export surfaces, and reporting-specific UI behavior.","Inspect OptionManager, DataStore, zoom and history behavior, thumbnail and export features, SSR and hydration seams, and accessibility surface.","Repurpose selected subsystems rather than the whole product. Mine the repo for chart runtime, option and data-store patterns, export and save-as-image behavior, zoom and history handling, and SSR reporting surfaces; keep what shortens build time, but rebind analytics schemas and application shell to the HyperTwist architecture.","","Repurpose here means: turn it into a bounded browser analytics and reporting layer for dashboards, replay summaries, and coaching views.","","","","","","","","","","","","Upgrade if source reveals unusually reusable browser-runtime seams, clean adapter boundaries, robust tests, and direct leverage for HyperTwist companion surfaces.","Demote if retained value is mostly commodity glue, tightly bound to upstream shell assumptions, or clearly dominated by stronger neighboring repos in the same lane.","Capability inventory + salvage targets + donor-tier verdict","1) Confirmed source-backed browser/XR capabilities; 2) Best retained modules; 3) Boundaries to keep outside HyperTwist core; 4) Recommended pairing or dependency posture; 5) Risks or licensing notes.","Primary: HT_browser_surface cluster packet plus the repo-specific Markdown dossier. Optional: companion browser/XR rows in the same cluster.","apache/echarts is placed in Donor Bench for HyperTwist because it provides the strongest current browser analytics and reporting stack with SSR, export, and serious data-store behavior. Recommended action remains repurpose, but the retained value is bounded reporting and coaching analytics rather than a product shell.","Useful subsystem donor for HyperTwist, primarily in the browser companion layer; strongest current analytics and reporting donor in the browser stack.","Audit apache/echarts as a browser analytics and reporting candidate for HyperTwist. Inspect the modular chart runtime, OptionManager and DataStore, export surfaces, zoom and history behavior, SSR path, and accessibility seams. Decide which parts should remain direct dependencies versus concept-only references.","HT_browser_surface_0002","HT_browser_surface","apache/echarts","","","","","","","","Apache-2.0","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","v6_unified_source_of_truth_pack","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is Apache-2.0 and direct use is allowed. Treat it as a bounded reporting and analytics donor.","Use directly as a bounded browser analytics dependency; keep HyperTwist data contracts and product logic outside the upstream shell.","Preserve LICENSE and NOTICE materials where required and review redistributed assets separately from the code license.","Usually unnecessary unless you later replace a narrow upstream layer for product-shaping reasons.","high","notice-file-review","yes","","","","","","","","","","","","","v6.3_markdown_backfill","Backfilled from canonized Markdown dossier pass on 2026-04-24.","implemented_live_permissive","landed_permissive_preserve","Phase 3R-C closed. Preserve as the landed first-party analytics, reporting, export, and progress-visualization lane; start future widening from the live-lane audit, then the 3R-C implementation packet, then REPO_LICENSE_TRACKING.md."
 "ecomfe/echarts-gl","https://github.com/ecomfe/echarts-gl","HyperTwist","Merge Bench","Browser 3D analytics and explainer surface","P3","","","","","","medium","Retain selective 3D analytics surfaces, but keep HyperTwist domain models, scene ownership, and broader product shell outside the upstream package.","integrate","moderate modification","Determine whether echarts-gl should remain the bounded 3D analytics extension beneath echarts and what should stay outside HyperTwist core.","3D chart exports, GL layer mounting, graph and flow surfaces, view helper behavior, interaction model","Inspect manifests, README/docs, src chart and component exports, GL helper modules, and integration points with echarts and zrender.","Inspect 3D and GL chart exports, GL layer mounting, graph and flow surfaces, view helper behavior, and chart-space interaction handling.","Integrate selected subsystems rather than the whole product. Mine the repo for 3D analytics and explainer surfaces, GL layer mounting, and view helper behavior; keep what shortens build time, but bind it to HyperTwist analytics schemas and browser companion UI.","","Integrate here means: merge bounded 3D analytics and explainer surfaces into the browser reporting stack.","","","","","","","","","","","","Upgrade if source reveals unusually reusable browser-runtime seams, clean adapter boundaries, robust tests, and direct leverage for HyperTwist companion surfaces.","Demote if retained value is mostly commodity glue, tightly bound to upstream shell assumptions, or clearly dominated by stronger neighboring repos in the same lane.","Capability inventory + salvage targets + donor-tier verdict","1) Confirmed source-backed browser/XR capabilities; 2) Best retained modules; 3) Boundaries to keep outside HyperTwist core; 4) Recommended pairing or dependency posture; 5) Risks or licensing notes.","Primary: HT_browser_surface cluster packet plus the repo-specific Markdown dossier. Optional: companion browser/XR rows in the same cluster.","ecomfe/echarts-gl is placed in Merge Bench for HyperTwist because it adds bounded 3D analytics and explainer value on top of echarts. Recommended action remains integrate, but the retained value is selective 3D reporting and graph surface behavior rather than a product runtime.","Useful merge candidate for HyperTwist, primarily in the browser companion layer; strongest value sits in 3D chart and GL explainer surfaces beneath the echarts lane.","Audit ecomfe/echarts-gl as a browser 3D analytics companion to apache/echarts. Inspect the 3D chart exports, GL view handling, and interaction seams. Decide which pieces deserve direct dependency use versus conceptual guidance only.","HT_browser_surface_0003","HT_browser_surface","ecomfe/echarts-gl","","","","","","","","BSD-3-Clause","known_from_reference_material","uploaded_reference_docs","","","","","","","","","","","v6_unified_source_of_truth_pack","","","","permissive_or_noncopyleft_known","direct_incorporation_ok","The code license is BSD-3-Clause and direct use is allowed. Treat it as a bounded 3D analytics extension rather than a runtime foundation.","Use directly as a bounded browser-side dependency beneath the reporting stack; keep HyperTwist scene ownership and product logic outside the upstream shell.","Typically preserve notices, attribution, and license text where required.","Usually unnecessary unless you later replace a narrow upstream layer for product-shaping reasons.","high","standard-notice-review","yes","","","","","","","","","","","","","v6.3_markdown_backfill","Backfilled from canonized Markdown dossier pass on 2026-04-24.","selected_not_live_permissive_candidate","phase2rb_analytics_subordinate_gap_eval_only","Phase 2R-B plus the 2026-05-27 analytics and browser-viewer subordinate-stack clarification are closed. Retain only as the optional subordinate 3D analytics and explainer sidecar beneath the landed apache/echarts lane; no default widening packet is open."