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CI gate G1 (scripts/check_paths.py) failed with 8 unresolvable references. Both command files referenced `scripts/<tool>.py` and `assets/<file>` as if they were relative to the command file, but commands/ sits at the plugin root while the scripts live under skills/memory-engineering/. SKILL.md was correct already — it sits inside the skill directory, so its bare `scripts/...` paths resolve — which is why this only showed up in the two command files. Rewritten to the plugin-root-relative form (`skills/memory-engineering/scripts/...`), matching how agent-harness writes its command paths. check_paths.py --all now reports 0 findings across 586 files. Also re-ran the other five blocking gates locally: check_plugin_json, check_dual_publish, smoke_scripts, smoke_json_output, derive_counters --check — all pass, plus compileall on the plugin. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Jt1sqt5kQmopyfXu2Hhjnv
77 lines
2.9 KiB
Markdown
77 lines
2.9 KiB
Markdown
---
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description: Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.
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argument-hint: "[memory dir, design spec JSON, or a question about a memory system]"
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---
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# /cs:memory-engineering
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Run the memory-engineering pass on `$ARGUMENTS`.
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Load `engineering/memory-engineering/skills/memory-engineering/SKILL.md` and
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follow it. Report every script's exit code as a finding — a non-zero exit is a
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result to surface, never an error to swallow.
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## Pre-flight
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Establish these before running anything. If the user cannot answer 1 or 2,
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that gap **is** the first finding — say so rather than guessing:
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1. **Does a memory system exist yet, or is this a design?** Design → steps 1, 2, 4. Existing store → steps 1, 3, 4.
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2. **What leaves the store today?** If the answer is "nothing", skip to step 4; the gate result is the headline.
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3. **Is this actually a memory question?** Maintaining one markdown vault → `llm-wiki`. Nightly consolidation loop → `skillopt-sleep`. Bounding a task loop → `agent-harness`.
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## Pass
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**1. Price the write path**
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```bash
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python skills/memory-engineering/scripts/memory_cost_profiler.py --spec <workload.json>
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```
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Lead the report with the construction/query split and **cost per correct
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answer**. Never present accuracy on its own.
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**2. Choose which cost to pay**
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```bash
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python skills/memory-engineering/scripts/memory_architecture_picker.py --constraints <workload.json>
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```
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If it exits 2 (`AMBIGUOUS`), **stop and put the printed tie-breaking question to
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the user.** Do not pick for them — the tie is real, not a tooling limitation.
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**3. Audit the real store** (skip if this is a greenfield design)
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```bash
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python skills/memory-engineering/scripts/memory_density_auditor.py --dir <path>
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```
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Report the FACT/SKILL/LOG/PROSE split. Users are routinely wrong about how much
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of their store is transcripts.
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**4. Gate on forgetting** — blocking
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```bash
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python skills/memory-engineering/scripts/forgetting_policy_linter.py --policy <design.json>
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```
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Exit 4 is a **stop**. Name the failing check (F1 or F4) and its fix. Do not
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present a FAIL alongside a recommendation to proceed.
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## Output
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Report in this order — cost before quality, always:
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1. **Verdict** — one line, leading with the blocking result if there is one
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2. **Cost** — construction/query split, cost per correct answer, amortization
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3. **Architecture** — the family, and the cost it makes them pay
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4. **What the store holds** — the FACT/SKILL/LOG/PROSE split, duplicates, staleness
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5. **Forgetting gate** — PASS / CONDITIONAL / FAIL with the named failing checks
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6. **Next step** — exactly one, sequenced per the ship order
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Attribute every number to its source with a confidence level. Vendor customer
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figures are testimonials, not benchmarks — label them as such.
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For a structured walkthrough, hand the user
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`skills/memory-engineering/assets/memory_engineer_worksheet.md` (the seven forcing questions) and walk
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them **one at a time**.
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