Memory Checkup

Audit an agent's memory corpus (MEMORY.md, USER.md, and files under memory/) and surface consistency problems before they reach the user: stale superseded numbers, contradictions, dangling file references, abandoned daily entries, and orphan files.

When to run

1. The user asks to check/audit/clean memory ("体检", "check my memory").
2. Before answering questions that depend on facts recorded over many days (awards, counts, project statuses).
3. After a metric changes and the user asks to sync all documents (e.g. download counts in application essays).
4. Periodically, alongside daily memory flushes.

Procedure

Step 1: Locate the corpus

The corpus root is the agent workspace: it must contain MEMORY.md and/or a memory/ directory. Ask or infer from context; in OpenClaw this is the workspace directory.

Step 2: Run the audit script

python3 scripts/memory_checkup.py --memory-dir <workspace-root> [--json]

The script scans and reports:

Check	Meaning
Stale numbers	Same metric (downloads/stars/users) with an old value in some files and a newer value in newer files
Dangling references	File paths mentioned in memory that no longer exist
Stale entries	Daily-flush files whose date has a gap to the newest entry
Orphans	memory/ files never referenced from anywhere else

Step 3: Verify before editing

For every stale-number finding, confirm the current value with the user or with live evidence (run the real check: hit the API, count the files). Never assume the newest mention is correct just because it is newest.

Step 4: Fix

• Update stale mentions, or annotate them as superseded if history should be preserved ("旧口径 4,600+ 已作废").
• Remove or repair dangling references.
• Do NOT delete orphan daily files; list them to the user and let them decide.
• After edits, re-run the audit to confirm zero issues.

Step 5: Report

Summarize in the user's language: how many files scanned, issues by type, what was fixed, what needs a user decision.

Notes

• The script is read-only. All fixes are made by the agent, file by file, after verification.
• Number extraction currently covers downloads/stars/users patterns (English and Chinese); extend DEFAULT_PATTERNS for other metrics.
• JSON output is machine-readable for piping into other tools.