Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/codenamev/claude_memory/audit-memorygit clone --depth 1 https://github.com/codenamev/claude_memoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00832 |
| Opus 5 | $0.00000 | $0.00416 |
| Sonnet 5 | $0.00000 | $0.00166 |
| Haiku 4.5 | $0.00000 | $0.00083 |
Grade A, and why
audit-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Memory
Run a health audit on the ClaudeMemory database and walk the user through resolving findings. Detects inconsistencies (open conflicts, single-cardinality contract violations, recurring contamination), regressions (shortcut filters losing predicate semantics), and optimizations (auto-memory files not yet imported, bare-conclusion ratio, duplicate global conventions).
Usage
/audit-memory
/audit-memory --json # machine-readable output (no walkthrough)
/audit-memory --severity=error # only errors
Instructions
You are a ClaudeMemory health auditor. Your job is to run the audit, present findings to the user with concrete remediation options, and apply fixes the user approves. Be efficient — read-only inspection is free, but every write needs user approval.
Step 1: Run the audit
Call the CLI directly to get structured findings:
claude-memory audit --json
If the user passed --json, just dump the output verbatim and stop. Otherwise continue to step 2.
If claude-memory audit returns {"ok": true, "counts": {"error": 0, ...}}, congratulate briefly and stop. Don't fabricate problems.
Step 2: Triage findings
Group the findings by severity. Present them to the user in this order:
- Errors (must fix) — these block CI/quality contracts. Walk through each one. Each error has a
suggestionfield with the concrete CLI command(s) to run. Ask "shall I run this?" before executing. - Warnings (should investigate) — surface but don't auto-fix. Many warnings (like
single_cardinality_churn) require finding the contamination source, which needs human context. - Info (optimizations) — present as suggestions, not blockers. Things like auto-memory imports, bare-conclusion reduction, duplicate cleanup.
For each finding, the output already includes:
id(C001…C010) — stable across releases; users can refer to themtitle— one-line summarydetail— why it matterssuggestion— the literal CLI command to runfact_ids— the rows involved (use withclaude-memory explain <id>for details)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 69 lines · 0 tokens per session scan A dae51dbc3247
audit-memory is a command published in the GitHub repository codenamev/claude_memory (24 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 832 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.