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
New engineering/memory-engineering/ plugin. The repo had no skill for
designing, pricing, or auditing an agent memory system: llm-wiki maintains
one vault, skillopt-sleep runs a consolidation loop, agent-harness bounds a
task loop. This bounds a store.
Four stdlib scripts, one per lens:
- memory_cost_profiler.py — construction vs query split, cost per correct
answer, amortization ratio, construction co-location warning
- memory_architecture_picker.py — scores the four paradigm families,
disqualifies on hard constraints, names the cost the choice makes you pay,
and refuses to pick when the top two tie (exit 2 + tie-breaking question)
- memory_density_auditor.py — classifies records FACT/SKILL/LOG/PROSE, finds
near-duplicates, flags staleness and time-relative wording, scores density;
runs on a real --dir or --jsonl
- forgetting_policy_linter.py — the gate. 8 checks; F1 (explicit forgetting
rule) and F4 (contradictions surfaced, never auto-merged) block at exit 4
Evidence discipline: the four-lens framing is synthesized from @N01ennn's
"How to be a Memory Engineer", but every quantitative claim is re-cited to
the primary source, and two of the article's paraphrases are corrected in the
references rather than propagated — the 47x energy figure is the spread across
ten evaluated systems (not an accuracy-matched pair), and the 97%
first-pass-error figure is Rakuten's named vendor testimonial (not a
controlled study). Per-claim confidence levels throughout.
Three classifier defects found and fixed during the build, each of which would
have produced garbage on a real repo: markdown headings inside fenced code
blocks were splitting records (258 phantom records -> 107 on a real directory);
short fragments matched trivially at 1.00 Jaccard (41 false-positive
duplicates); and signal-less prose was labeled LOG, firing LOG_HEAVY at 74% on
a documentation folder — now its own PROSE class.
Also ships 4 references (7 sources each), a seven-question forcing worksheet,
a combined example spec consumed by all three spec-taking scripts, an F1–F8
policy template, cs-memory-engineer, /cs:memory-engineering and
/cs:forgetting-audit.
Verified: all 4 scripts pass --help / --sample / --output json, stdlib-only,
correct exit codes on error paths; SKILL.md 6/6 PASS on the write-a-skill
checklist; check_plugin_json.py --all and derive_counters.py --check both pass.
Counters: skills 362->363, tools 644->648, refs 741->745, agents 102->103,
commands 116->118, plugins 88->89.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Jt1sqt5kQmopyfXu2Hhjnv
