System Audit

You are running a quality / sanity / efficiency audit on Ane's MEL/SRHR system. Output is a Tier 1 working brief listing issues with file paths and a sequencing recommendation. You do NOT execute fixes. Ane reviews and confirms each.

When to use this

• Ane types /system-audit, /audit, /system-check, or asks for a system health read
• Before any /li curate (audit surfaces issues curate should consolidate)
• After substantive agent-improvement work to verify nothing drifted
• Periodically as a backstop against silent drift

This skill complements rather than duplicates /test (harness only) and /li lint (wiki only). It is the broader pass that catches inconsistencies between layers.

Workflow

Step 1 — Harness state (fast, mechanical)

Run in this order:

1. python tests/run_tests.py (static; expect 78/78 or higher as system grows)
2. python tests/run_tests.py --output (fixture mode; some fixtures may be deliberately uncaptured)

Parse failures. Each [FAIL] line goes into the findings list with file:line as shown. Do NOT re-run the failures or attempt fixes; just record.

Step 2 — Documentation consistency (medium severity, high impact)

Read in parallel:

• ~/.claude/CLAUDE.md — scan for self-contradictions. Common patterns: specialist count drift ("16" vs "20" in same file), version-number drift, layer-table drift.
• <work folder>/CLAUDE.md — scan for stale numbers (specialist count, harness check count, page count, agent-team member count).
• ~/.claude/skills/{ann,vi,li,researcher}/SKILL.md — line counts vs budgets (Ann ≤ 250; Vi ≤ 200; Li ≤ 400; Researcher ≤ 200). Em-dash counts in body prose (per CLAUDE.md voice rule).
• agent-improvements/agent_registry.md — count ### entries. Cross-reference with Vi taxonomy table for completeness. Cross-reference each name with ~/.claude/agents/<name>.md existence.

For each finding, capture: file path, line number where shown, what's wrong, suggested fix from the failure-fix table below.

Step 3 — Overlay and CURATE state (medium severity)

Read in parallel:

• agent-improvements/{ann,vi,li,researcher}-overlay.md — file sizes (cap 35KB) and Active-entries count (cap 10). If either breaches: flag for compression CURATE, not just archive-only CURATE.
• agent-improvements/coordination-log.md — count entries with STATUS: OPEN.
• agent-improvements/_pending-ingest.md — count rows with Status: PENDING. Surface to Ane for action.
• agent-improvements/PROPOSED-*.md — Status: field on each. Flag any AWAITING APPROVAL older than 7 days.

Step 4 — Hygiene (low severity, visible clutter)

Check:

• agent-improvements/_temp_*.md — any files older than 7 days are stale candidates. Use ls -la to get dates.
• agent-improvements/proposed-agents/*.md — for each .md (excluding README), check whether ~/.claude/agents/<same-name>.md exists. If yes, the staging copy should have been removed when deployed. Use diff -q to confirm divergence.
• agent-improvements/SESSION-STATE-*.md — read each; check for internal contradictions (table cells contradicting bullet points, or progress-status fields disagreeing).
• mel_wiki/wiki/raw/ size — should not be empty (the immutability rule says Li reads but never modifies; if it's empty, ingestion has stopped).

Step 5 — Architectural ceilings (decisions, not bugs)

Read:

• agent-improvements/qa-disagreement-log.md — count rows. If 0, the Vi/Li elevation watch-trigger has not fired. Note as "ceiling unmonitored except via watch".
• agent-improvements/cost-calibration-log.md — count rows with observed actuals vs not observed. If less than 30% have actuals, observability remains weak.
• mel-framework-reference.md size (claude-ai mirror) — flag if grown >150KB (it was 117KB at 2026-04-28 audit; bigger means drift surface increased).
• Em-dash count across wiki body prose — grep -c "—" on mel_wiki/wiki/**/*.md. Compare to most recent audit-drift baseline. Trend matters more than absolute count.

Step 6 — Recent audit-drift carry-forward

If agent-improvements/audit-drift-*.md exists:

1. Read the most recent (sort by filename date).
2. For each previously-flagged item, check whether it has been resolved.
3. Carry-forward unresolved items into your findings, marked [carry-forward from audit-drift YYYY-MM-DD].

This prevents the same issue from getting re-flagged as new each audit and from being silently dropped between audits.

Failure-fix mapping (your quick reference)

Issue category	Most likely fix
Specialist count drift in CLAUDE.md	Replace stale number with current agent_registry.md count
Vi taxonomy table missing specialist	Add row matching agent_registry.md entry
Vi model rule conflict with registry	Align Vi to registry's model_default field; registry is authoritative
Skill file over budget	Compress narrative; move detail to wiki; edit Ann/Vi/Li/Researcher skill cap in tests/run_tests.py only as last resort
Em-dash in body prose	Convert per mel-report-writer's worked patterns (apposition → comma; list introducer → colon; sentence break → period)
Stale _temp_*.md file	git rm if older than 7 days and not currently referenced
Deployed agent in proposed-agents/	git rm the staged copy; live in ~/.claude/agents/ is canonical
Overlay over 35KB cap	Run substantive CURATE (compress entries, not just archive); Li's archive-only pass is insufficient at this size
qa_block schema-vs-skill mismatch	Schema is authoritative; align skill text to schema field names

Output format

Tier 1 working brief. BLUF in first sentence. No em-dashes (you are auditing for em-dash discipline; do not violate the rule you are checking). Plain English. Active voice. Sentences ≤ 25 words. Per CLAUDE.md "Tier 1 working brief" rules.

Use this template:

# System audit — YYYY-MM-DD

**BLUF: [N issues found, M severity-medium, P severity-low, Q architectural decisions. None blocking. X are quick fixes.]**

## Tests run
- python tests/run_tests.py: N/N
- python tests/run_tests.py --output: M/N (deferred fixtures noted)
- Manual audit of: [list]

## Documentation drift (medium severity)

| # | Issue | File | Fix |
|---|---|---|---|

## Hygiene (low severity)

| # | Issue | Evidence | Fix |
|---|---|---|---|

## Architectural ceilings (decision points)

| # | Issue | Implication |
|---|---|---|

## What is working

- [5-8 bullet items: harness clean, qa_block schema operational, P1 triple-load fix landed, etc.]

## Recommended sequencing

- Quick batch (~30 min): [issue numbers; mechanical edits]
- Focused pass (~1 hour): [issue numbers; need judgement]
- Strategic decision: [issue numbers; require Ane's call]

**Evidence base:** [inline file paths]

What NOT to do

• Do not auto-execute fixes. Ane reviews and runs each one.
• Do not flag known deferred items as new issues. Cross-check agent-improvements/system-audit-*.md and audit-drift-*.md for what is already documented and accepted.
• Do not duplicate the harness output. If python tests/run_tests.py covers a check statically, just report the harness result; do not re-derive the finding.
• Do not produce a grade. That is /grade-system.
• Do not flatter the system. Honest finding count, honest severity.

Closing

End the brief with: "Want me to execute the quick-batch fixes now?" Then wait. Ane confirms before any execution.

If Ane confirms, execute the quick batch (mechanical edits). For focused-pass items, ask one more confirmation per item — those involve judgement that Ane should approve case by case. For strategic-decision items, never auto-execute.

Cost band

~30-60k tokens for a clean audit pass. Larger if many findings need cross-reference verification. Within the 200k system-improvement cap with substantial headroom.