deep-audit

name	deep-audit
description	Deep consistency audit of the entire repository infrastructure. Launches 4 parallel specialist agents to find factual errors, code bugs, count mismatches, and cross-document inconsistencies. Then fixes all issues and loops until clean. Use when: after making broad changes, before releases, or when user says "audit", "find inconsistencies", "check everything".
author	Claude Code Academic Workflow
version	1.0.0
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep","Task"]
disable-model-invocation	true

/deep-audit — Repository Infrastructure Audit

Run a comprehensive consistency audit across the entire repository, fix all issues found, and loop until clean.

When to Use

• After broad changes (new skills, rules, hooks, guide edits)
• Before releases or major commits
• When the user asks to "find inconsistencies", "audit", or "check everything"

Workflow

PHASE 0: Mechanical checks (run FIRST, cheap, deterministic)

Before spawning agents, run the mechanical parity checks:

python3 scripts/check-skill-integrity.py --verbose

This catches four classes of bug that agent-based audits have historically missed:

1. Frontmatter allowed-tools ↔ body tool-invocation parity (e.g. body spawns Task but Task not in allowed-tools — the v1.7.0 PR #92 miss).
2. argument-hint ↔ body flag parity (flags documented but not advertised, or vice versa).
3. Internal markdown anchors resolve (no broken [text](path#anchor) links — the #category-11-numerical-discipline miss on PR #87).
4. Rule paths: ↔ skill implementation parity (rule claims skill follows protocol but skill body has none of the protocol keywords — the /interview-me miss on PR #92).

If Phase 0 reports P0 or P1 findings, fix them (or tune the regex if they are false positives) before launching the 4 agents. The mechanical layer is cheaper and more precise than agent prompts for these classes.

PHASE 1: Launch 4 Parallel Audit Agents

Launch these 4 agents simultaneously using Task with subagent_type=general-purpose. Each agent's prompt must tell it to read .claude/references/audit-pet-peeves.md and explicitly check for each class of bug before reporting clean. The pet-peeves file is a living catalogue of drift patterns review bots have caught; it grows with each PR.

Agent 1: Guide Content Accuracy

Focus: guide/workflow-guide.qmd

• All numeric claims match reality (skill count, agent count, rule count, hook count)
• All file paths mentioned actually exist on disk
• All skill/agent/rule names match actual directory names
• Code examples are syntactically correct
• Cross-references and anchors resolve
• No stale counts from previous versions

Agent 2: Executable Code Quality

Focus: all executable code in the repo — .claude/hooks/*.py, .claude/hooks/*.sh, scripts/*.py, scripts/*.sh, .claude/scripts/*.sh. Not just .claude/hooks/ — when PR #93 added new code under scripts/, the original narrow scope meant Copilot + Codex caught 5 bugs the audit missed.

Hook-specific checks (Stop/PreToolUse/SessionStart protocols, CLAUDE_PROJECT_DIR usage, hash-length consistency) apply only to .claude/hooks/. Everything below applies to ALL executable code:

• No remaining /tmp/ usage in anything that manages state (should use ~/.claude/sessions/)
• Hash length consistency ([:8] across all hooks) [hooks only]
• Proper error handling — fail-open pattern where the docstring promises it (top-level try/except with sys.exit(0)). Python read_text() must catch UnicodeError (not just OSError) if the script is promised fail-open for corrupt files. Bash set -u without set -e or explicit post-command checks does NOT catch command failures — verify.
• Docstring-claim ↔ implementation parity. If a function's docstring describes "bidirectional parity" / "fail-open" / "exits 1 on X", the implementation must match. Common drift: one-directional implementation of a claimed-bidirectional contract; exit codes documented as one thing but returning another.
• Config-map entries point at live targets. Keyword dicts, path maps, and rule registries should not contain dead entries (e.g. rule files that don't exist, fields the script doesn't actually read). Dead entries mislead maintainers.
• JSON input/output correctness (stdin for input, stdout/stderr for output) [hooks only]
• Exit code correctness. Two valid blocking protocols for Stop/PreToolUse hooks: (a) exit 2 + reason on stderr — legacy, still supported (b) exit 0 + JSON {"decision": "block", "reason": "..."} on stdout — modern; this is what log-reminder.py uses and it works correctly Non-blocking hooks always exit 0. PreCompact hooks MUST exit 0 (stdout is discarded by the harness — use stderr for diagnostics)
• from __future__ import annotations for Python 3.8+ compatibility
• Correct field names from hook input schema (source not type for SessionStart)
• PreCompact hooks print to stderr (stdout is ignored)

Agent 3: Skills and Rules Consistency

Focus: .claude/skills/*/SKILL.md and .claude/rules/*.md

• Valid YAML frontmatter in all files
• No stale disable-model-invocation: true
• allowed-tools values are sensible
• allowed-tools actually covers every tool the skill body invokes. For every Task spawn, Bash command, Write/Edit call mentioned in the skill's Steps / Phases / Workflow body, verify the tool appears in the allowed-tools array. Common miss: skill body says "spawn agent-X via Task with context=fork" but Task is absent from allowed-tools — runtime permission error or silent bypass. Caught this class of bug after Codex/Copilot flagged it on PR #92 (4 skills promised Task in their Post-Flight sections but 3 of 4 had no Task permission).
• Rule paths: scope matches skill implementation. If rule X lists skill Y in paths:, verify skill Y actually implements the protocol rule X mandates. A rule claiming a skill follows a protocol is meaningless if the skill doesn't.
• Rule paths: reference existing directories
• No contradictions between rules
• CLAUDE.md skills table matches actual skill directories 1:1
• All templates referenced in .claude/rules/*.md and the guide (guide/workflow-guide.qmd) exist in templates/

Agent 4: Cross-Document Consistency

Focus: README.md, docs/index.html, docs/workflow-guide.html

• All feature counts agree across all 3 documents
• All links point to valid targets
• License section matches LICENSE file
• Directory tree matches actual structure
• No stale counts from previous versions

PHASE 2: Triage Findings

Categorize each finding:

• Genuine bug: Fix immediately
• False alarm: Discard (document WHY it's false for future rounds)

Common false alarms to watch for:

• Quarto callout ## Title inside ::: divs — this is standard syntax, NOT a heading bug
• allowed-tools linter warning — known linter bug (Claude Code issue #25380), field IS valid
• Counts in old session logs — these are historical records, not user-facing docs
• Counts in CHANGELOG.md under past version headings — those are snapshots; do NOT update
• log-reminder.py outputting {"decision": "block"} with sys.exit(0) — this IS the modern Claude Code Stop-hook block protocol, NOT a bug

Count drift specifically: search for every phrasing variant. A common failure mode is that replace_all on one phrasing (e.g., "26 skills") misses sibling phrasings in the same repo. When checking counts, grep for ALL of:

• "N skills", "N skill " (with space)
• "N slash commands"
• "N specialized" (as in "N specialized agents")
• "template's N" (informal count in prose)
• Commas/conjunctions: "skills," vs "skills, and" are treated as different strings by replace_all Verify zero matches for the OLD number across the whole tree before declaring clean.

PHASE 3: Fix All Issues

Apply fixes in parallel where possible. For each fix:

1. Read the file first (required by Edit tool)
2. Apply the fix
3. Verify the fix (grep for stale values, check syntax)

PHASE 4: Re-render if Guide Changed

If guide/workflow-guide.qmd was modified:

quarto render guide/workflow-guide.qmd
cp guide/workflow-guide.html docs/workflow-guide.html

PHASE 5: Loop-until-dry or Declare Clean

After fixing, launch a fresh set of 4 agents to verify. This is the loop-until-dry primitive (orchestrator-protocol.md):

• Converge when a round surfaces 0 new genuine issues (deduped on file+issue) — declare clean and report summary.
• If new issues found → fix and loop again.
• Fallback cap: 5 loops bounds a non-converging audit (prevents infinite cycling); a finding that survives rounds N and N+2 is escalated to the user rather than re-patched (summary-parity.md).

Key Lessons from Past Audits

These are real bugs found across 7 rounds — check for these specifically:

Bug Pattern	Where to Check	What Went Wrong
Stale counts ("19 skills" → "21")	Guide, README, landing page	Added skills but didn't update all mentions
Hook exit codes	All Python hooks	Exit 2 in PreCompact silently discards stdout
Hook field names	post-compact-restore.py	SessionStart uses source, not type
State in /tmp/	All Python hooks	Should use ~/.claude/sessions/<hash>/
Hash length mismatch	All Python hooks	Some used [:12], others [:8]
Missing fail-open	Python hooks __main__	Unhandled exception → exit 1 → confusing behavior
Python 3.10+ syntax	Type hints like `dict	None`
Missing directories	quality_reports/specs/	Referenced in rules but never created
Always-on rule listing	Guide + README	meta-governance omitted from listings
macOS-only commands	Skills, rules	open without xdg-open fallback
Stale hook references	Rules, guide, CHANGELOG, settings.json	Removed hooks still mentioned somewhere

Output Format

After each round, report:

## Round N Audit Results

### Issues Found: X genuine, Y false alarms

| # | Severity | File | Issue | Status |
|---|----------|------|-------|--------|
| 1 | Critical | file.py:42 | Description | Fixed |
| 2 | Medium | file.qmd:100 | Description | Fixed |

### Verification
- [ ] No stale counts (grep confirms)
- [ ] All hooks have fail-open + future annotations
- [ ] Guide renders successfully
- [ ] docs/ updated

### Result: [CLEAN | N issues remaining]