review-memory

name	review-memory
description	Use this skill when inspecting RECALL memory quality, when reviewing current memory state, when auditing noise or conflicts, or when gathering memory IDs before maintenance. Use proactively for inspection-only work, not mutation or new-memory capture.

Review Memory

Use this skill when the user asks what RECALL currently remembers, asks for a memory audit, or needs IDs before changing memory state.

RECALL is local-only project memory. Read from the active project's RECALL memory directory: .recall/ for new projects, or existing .codex_memory/ stores for legacy projects. Never require hosted services or external APIs. Do not repeat secrets verbatim if a stored memory appears to contain sensitive data.

When To Use

Use this skill for:

• broad "what does RECALL know?" questions
• quality checks on noisy or stale memory
• category, status, or source breakdowns
• collecting IDs before lifecycle updates
• verifying whether cleanup is needed after heavy tool use

Do not use this skill to create or mutate memory directly. It is an inspection and audit skill.

Execution Path

Use the public RECALL adapter:

Run these examples from the installed/source plugin root so ./scripts/recall_skill.py resolves. If the shell is in the active project repository, do not look for ./scripts there; use the adapter's absolute path and pass --root <project-root> when needed.

python ./scripts/recall_skill.py review-memory --limit 20
python ./scripts/recall_skill.py audit-memory --limit 20

Use recall_skill.py only. Treat lower-level backend scripts as internal support code.

Contract

This skill receives an inspection request and returns evidence: counts, IDs, quality signals, and suggested follow-up. It does not mutate memory. If the user asks to edit, archive, confirm, repair, or delete records, collect the needed IDs here and then hand the action to manage-memory.

Use the contract asset as the quick boundary check:

{"asset":"assets/contract.json","kind":"inspection-boundary"}

Inputs

This skill usually receives:

Input shape	Use it for	Example
broad review request	default inspection	"what does recall currently know?"
quality or noise concern	audit mode	"is memory noisy?"
narrow slice request	filtered review	"show stale risks"
follow-up maintenance task	ID gathering	"which memories should I prune?"

Filters

python ./scripts/recall_skill.py review-memory --status active --category requirements
python ./scripts/recall_skill.py review-memory --source finalizer --limit 50
python ./scripts/recall_skill.py review-memory --status stale --status superseded

Workflow

1. Start with review-memory --limit 20 when the request is broad.
2. Use audit-memory --limit 20 when the user wants signal-vs-noise diagnosis, archive candidates, or noisy command patterns.
3. Add --status, --category, or --source filters when the user asks about a specific slice.
4. Use returned IDs with manage-memory commands such as confirm-memory, resolve-memory, stale-memory, supersede-memory, merge-memories, or prune-memory.
5. Treat the review as evidence to inspect, not as guaranteed truth over the current repository state.

Result Handling

Report counts and the most relevant IDs. Prefer concise summaries over dumping every record. If many low-value automatic memories are present, recommend archive-noise for non-destructive cleanup and reserve prune-memory for targeted archival by ID.

Output Format

Return a compact review summary that includes:

• the request scope
• key counts by status, source, or category when relevant
• the most useful IDs
• any cleanup or follow-up recommendation

{
  "mode": "audit-memory",
  "shown": 6,
  "top_ids": [12, 18, 24],
  "recommendation": "archive-noise"
}

Examples

Broad review:

python ./scripts/recall_skill.py review-memory --limit 20

Noise-focused audit:

python ./scripts/recall_skill.py audit-memory --limit 20

Focused stale memory slice:

python ./scripts/recall_skill.py review-memory --status stale --category risks --limit 20

Conflict-focused inspection:

python ./scripts/recall_skill.py list-conflicts

Quality audit before cleanup:

python ./scripts/recall_skill.py audit-memory --limit 50

Decision Guide

Request type	Preferred command	Why
general memory inventory	review-memory	balanced overview
signal-vs-noise diagnosis	audit-memory	highlights noisy patterns
filtered category slice	review-memory with filters	narrower evidence set
maintenance prep	review-memory first	collect IDs before changes

Troubleshooting

Common Issues

• If the result set is too broad, add --status, --category, or --source.
• If IDs look outdated, verify repository reality before changing memory state.
• If the audit shows lots of low-value command records, recommend archive-noise rather than deleting records outright.
• If the user actually wants to edit or resolve a memory, switch to manage-memory once the needed IDs are known.
• If a record appears to contain a secret, report the ID and risk without repeating the value.
• If review output contradicts the current repository, label the memory stale-candidate instead of treating it as current truth.

Related

See Manage Memory for cleanup, lifecycle changes, and repair work after inspection. See Retrieve Memory for query-driven lookup when the user already knows the topic. See Save Insight for creating a new durable memory when the review reveals something missing. See Audit signals for quality and noise indicators. Sibling routes: skills/manage-memory, skills/retrieve-memory, skills/save-insight.