Memory

Skill tarangdeep-goel-by/claude-harness/claude/skills/memory

Dev-focused Claude Code harness — workflow-engine skills, in-repo memory, session continuity (/recall ↔ /vault-push), local telemetry, + a ready-to-fill vault scaffold. clone + ./install.sh and go.

Install

npx -y skills add tarangdeep-goel-by/claude-harness --skill memory

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Memory corpus quality rollup. /memory health reports capture/approval/utilization/hook-health; /memory review proposes harness tweaks.

SKILL.md

3.7 KB, 905 tokens by cl100k_base, as published. Nobody here has run it

/memory — Memory Corpus Quality

Read-only rollup of how the memory corpus is doing: are candidates being captured, are they being approved, is the corpus being read, are the hooks healthy. Distinct from /reflect (which applies/dismisses individual candidates) and /recall (which queries memory) — this is the audit view, like /infra-health for hooks and /stats for cost.

Commands

/memory health — corpus quality rollup

python3 ~/.claude/skills/memory/scripts/memory_health.py           # text report
python3 ~/.claude/skills/memory/scripts/memory_health.py --json    # one JSON object

Prints a read-only report across seven sections + a one-line verdict. Never mutates the corpus, the review queue, or the logs; degrades gracefully on every missing source (prints "(no data yet)", never errors).

Sections:

Capture — memory-infer hook: run count, total candidates yielded (sum of appended=N from hook detail), yield distribution (0/1/2/3+ per run).
Approval rate — over resolved queue entries: applied / (applied+dismissed), pending tracked separately. "(no resolves yet)" until the first candidate is applied or dismissed.
Invalidations — queue kind=invalidation count, split stale-90d vs overlap (parsed from description).
Utilization — from memory-consulted.json: % of corpus never consulted, top-5 consulted, count consulted in the last 30d. "(no data yet — read hook not yet active)" when the sink is absent.
Corpus shape — total / no-frontmatter / flat-schema / canonical; type distribution; confidence distribution; last_verified age histogram (none / <30d / 30-90d / >90d); superseded_by count.
Hook health — per memory hook: run count, error rate, mean duration.
Issues — never-consulted>60d, fastest-staling (high-confidence by age, top 5), zero-yield infer sessions, schema-adoption %.

Verdict — memory: HEALTHY or memory: NEEDS ATTENTION (N flags). Flags fire when: any hook error rate >10%, canonical adoption <10%, or never-consulted >50%.

/memory review — propose harness tweaks

python3 ~/.claude/skills/memory/scripts/memory_review.py

Reads the /memory health findings and proposes concrete, accept/defer harness tweaks — each as finding → why → action. Advisory and deterministic: it changes nothing. Accepting a tweak means making it a follow-up task.

Typical proposals: low schema adoption → run the S3 backfill; high never-consulted % → archive cruft / reseed warm-start; >25% zero-yield infer sessions → raise the turn threshold or tighten the prompt; low approval rate → tighten the prompt; hook error rate >10% → inspect the hook log; staling memories → re-verify via /reflect.

Data sources (all read-only)

Source	Holds
`~/vault/memory-review-queue.jsonl`	candidate + invalidation entries with `status` (pending/applied/dismissed)
`~/vault/logs/hooks.jsonl`	memory-infer / memory-validate / memory-staleness / memory-consulted hook events
`~/vault/logs/memory-consulted.json`	`{relpath: {count, last_seen}}` — read tracking (may not exist yet)
`~/.claude/projects/<project-slug>/memory/*.md`	the corpus itself (skips `MEMORY.md`, `SCHEMA.md`, `_shared.md`, dotfiles, `legacy/`)
`~/vault/logs/memory-staleness-state.json`	slug→date debounce map for the staleness checker

--json emits one object {capture, approval, invalidations, utilization, corpus, hooks, issues, verdict} for the downstream /memory review command.