Agent Memory Cleanup

Audit, clean, consolidate, and maintain long-term user memory files for OpenClaw, Hermes Agent, Codex, Claude, and other agents. Use when the user asks to clean, prune, sanitize, deduplicate, review, repair, or periodically maintain user.md, memory.md, memories.md, profile.md, preferences.md, or agent memory notes. Also trigger proactively when memory writes fail because files are too long, memory budget/context limits are exceeded, storage is full, updates are rejected, duplicates or conflicts accumulate, or memory is polluted by outdated project details, one-off task notes, stale plans, or conversation residue. Gives cleanup recommendations without requiring the user to name the skill, applies edits only after appropriate authorization, and creates recoverable backups.

This skill should be lightweight and mostly invisible. Do not announce that a skill is being used unless the user asks. The user only needs to see a plain memory-quality prompt when action is useful.

Default Flow

1. Detect memory pressure or pollution.
2. If Python/file access is available, run a cheap summary check first:

python scripts/audit_memory.py memory.md --summary-json

3. If quality.intervention is no_intervention_needed, do not interrupt the user.
4. If intervention is needed, say briefly that memory appears too long, duplicated, conflicted, stale, or unsafe, and ask whether to review cleanup recommendations.
5. After the user agrees, run:

python scripts/audit_memory.py memory.md --mode propose-patch --include-diff

6. Apply only after a second explicit approval, unless unattended cleanup was already authorized:

python scripts/audit_memory.py memory.md --mode apply-approved

The apply mode must create timestamped backups before writing.

Trigger Points

Use this flow for:

• Memory write/update rejected, full, over budget, truncated, or too long.
• Short memory with secrets, task-state residue, duplicated facts, or conflicting preferences.
• User says a remembered fact is wrong, outdated, project-only, or should not be remembered.
• Before saving a new global memory candidate:

python scripts/audit_memory.py --candidate "candidate memory text" --summary-json

If candidate lint returns do_not_write_candidate_to_global_memory, do not store it globally. Offer to skip it or keep it as project/task notes.

Intervention Values

• prompt_cleanup_now_secret_detected: recommend cleanup immediately; never echo raw secrets.
• prompt_user_review_conflicting_memory: ask the user to resolve conflicting durable preferences.
• do_not_write_candidate_to_global_memory: block global memory write.
• prompt_cleanup_recommended: offer cleanup recommendations.
• prompt_audit_recommended: mention memory quality may be degrading and ask whether to review.
• no_intervention_needed: stay silent.

Load Extra Context Only When Needed

Do not read references by default. Load them only for the matching need:

• references/default-rules.json: deterministic thresholds and regex rules.
• references/classification-rubric.md: manual fallback if Python cannot run.
• references/agent-paths.md: path discovery when memory files are unclear.
• references/mcp-version.md: MCP wrapper design.

Safety

• Keep only stable global preferences and durable cross-task context.
• Remove or redact secrets, stale task state, branch/PR/debug notes, and one-off plans.
• Do not rewrite clean memory just for style.
• Do not broadly scan the user home directory without explicit request.
• Back up every edited memory file.