Hook Audit

Hook audit

Step 0: Gather context

Run Bash commands before proceed:

• ls "$(git rev-parse --show-toplevel 2>/dev/null)/.claude/hooks/"*.sh 2>/dev/null | wc -l -- installed hook scripts
• ls ~/.claude/hook-metrics/*.json 2>/dev/null | wc -l -- session summaries collected
• ls ~/.claude/hook-metrics/*.json 2>/dev/null | head -1 | xargs -I{} jq -r '.date' {} -- earliest date
• ls ~/.claude/hook-metrics/*.json 2>/dev/null | tail -1 | xargs -I{} jq -r '.date' {} -- latest date

Metrics dir: ~/.claude/hook-metrics/

Your task

Analyze hook effectiveness across all session metrics. Read every JSON in ~/.claude/hook-metrics/. Produce report:

1. Hook activity

Each hook fired >=1 across sessions:

• Total blocks, warns, denies
• Avg fires per session
• Trend: up or down over time?

2. Silent hooks

List hook scripts in .claude/hooks/ with zero entries in any metrics file. Prune candidates -- never trigger or not wired to logging.

3. Over-aggressive hooks

High block counts hurt productivity:

• Blocks-per-session ratio > 3 -> flag too strict
• Same rule blocked repeat in one session -> agent retry and fail

4. Enforcement gaps

Cross-ref CLAUDE.md rules vs hook activity:

• Rules with hook but zero fires -> followed perfect or untested
• Rules with no hook -> advisory, no enforce

5. Recommendations

From data:

• Prune: hooks never fire (remove or merge)
• Soften: hooks block too much (demote to warn)
• Harden: warns fire often (promote to block)
• Add: CLAUDE.md rules with no hook enforce

6. Retro analytics (session flow)

Broader than hook-level. Pull from session JSONL + git log same window as metrics.

• Sessions -> PR lag: median time from first edit to PR open. High lag = planning thrash.
• CI first-try pass rate: PRs green on first CI run / total PRs. Low = hooks missed pre-commit catches.
• Phases skipped in /development-lifecycle: sessions wrote code without prior grill step (infer from session-touched-files + absence of grill markers). High skip = gate ineffective.
• Review-round distribution: how often hit 0/1/2/3 AI self-review rounds? Bulk at 3 = reviewer too picky or code quality trending down.
• Human-review resolution latency: time from human review comment -> resolved thread. High = bottleneck.
• Worktree sprawl: count active worktrees per repo. >4 sustained -> investigate with /mux --list candidates for prune.

Output per-metric: current value, 7-day trend (up/down/flat), actionable next step.

Mode flags

$ARGUMENTS:

• empty / --hooks -> run sections 1-5 only (default).
• --retro -> run sections 1-6 with emphasis on section 6.
• --all -> all sections, no emphasis.

Output format

Structured report. Tables where data fit. End with prioritized action list (max 5). If <5 session files, note data limited, recs preliminary.