health

name	health
description	Runs a budget-aware agent-assisted engineering health audit for instruction/config drift, hooks/MCP, verifier surfaces, and AI maintainability. Use when users ask 检查claude/检查codex/检查pi/配置检查/健康度 or report agents ignoring instructions, missing validation, or code becoming hard to maintain. Not for debugging code or reviewing PRs.
when_to_use	检查claude, 检查codex, 检查pi, Codex 配置, Pi 配置, AGENTS.md, config.toml, agent instructions, 健康度, 配置检查, 配置对不对, AI coding 腐化, 代码变烂, 维护性, 上下文混乱, 验证缺失, 验证命令失真, Claude ignoring instructions, Pi coding agent, check config, settings not working, audit config
dispatch_intent	Codex/Claude/Pi ignoring instructions, agent config audit, hooks/MCP broken, health token usage, AI coding code rot, hotspot ownership, unclear context, missing verification, stale verifier output

Health: Agent-Assisted Engineering Health

Prefix your first line with 🥷 inline, not as its own paragraph.

Audit the current project's agent setup and AI coding maintainability against this framework: agent config → instruction surfaces → tools/runtime → verifiers → maintainability

Find violations. Identify the misaligned layer. Calibrate to project complexity only.

Outcome Contract

• Outcome: a budget-aware health report that separates agent configuration risk from AI maintainability risk.
• Done when: each finding names the misaligned layer, the concrete evidence, and a copy-pasteable action or diagnostic command.
• Evidence: collected health script output, tracked project instructions, runtime config summaries, verifier logs, hooks/MCP surfaces, and live probes when needed.
• Output: prioritized findings with status, impact, and next action, or a clear clean bill with residual risk.

Two lanes share one report:

• Agent config health: Codex/Claude/Pi instruction drift, permissions, hooks, MCP, skills, and memory supply chain.
• AI maintainability health: project context surface, verifier wrapper, generated-artifact checks, hotspot ownership, and stale or misleading durable docs.

Output language: Check in order: (1) project agent instructions (AGENTS.md before runtime-specific files); (2) global agent instructions; (3) user's recent language; (4) English.

Budget posture: Start with the summary audit. Escalate automatically when the user asks for a deep, full, complete, thorough, "深入", "完整", "彻底", or "继续跑完" audit, when the user explicitly mentions AI coding code rot, Codex/Claude config drift, unclear context, missing verification, verifier output that points at stale paths, or "代码变烂", when current project instructions or remembered user preference says to run deep health checks by default, when the project is Complex, or when the summary pass exposes a critical ambiguity that cannot be resolved locally. Otherwise do not read full conversation extracts or launch inspector subagents. Tell the user before escalating because deep health audits can consume significant token quota.

Durable Context Preflight

See rules/durable-context.md for when to read durable context, the read-order budget, and the memory-type mapping.

For /health, audit expectations are decision, preference, and principle entries; checks for repeated failures are pattern and learning. Current CLAUDE.md, installed skills, hooks, MCP config, command output, and live probes override memory. Also flag durable memory problems when they affect behavior: oversized injected summaries, stale or contradictory entries, missing project entrypoint references, or private paths copied into public instructions. Keep these as context findings, not code-review findings.

Step 0: Assess project tier

Pick one. Apply only that tier's requirements.

Tier	Signal	What's expected
Simple	<500 files, 1 contributor, no CI	CLAUDE.md only; 0-1 skills; hooks optional
Standard	500-5K files, small team or CI	CLAUDE.md + 1-2 rules; 2-4 skills; basic hooks
Complex	>5K files, multi-contributor, active CI	Full six-layer setup required

Step 1: Collect data

Run the collection script in summary mode first. Do not interpret yet.

# Resolve collect-data.sh from canonical locations (no personal home-dir paths).
HEALTH_SCRIPT="${CLAUDE_SKILL_DIR:+$CLAUDE_SKILL_DIR/scripts/collect-data.sh}"
if [ ! -f "${HEALTH_SCRIPT:-}" ]; then
  for candidate in \
    "./skills/health/scripts/collect-data.sh" \
    "$(npx skills path tw93/Waza 2>/dev/null)/skills/health/scripts/collect-data.sh"; do
    [ -f "$candidate" ] && HEALTH_SCRIPT="$candidate" && break
  done
fi
if [ ! -f "${HEALTH_SCRIPT:-}" ]; then
  echo "health collect-data.sh not found; set CLAUDE_SKILL_DIR or reinstall: npx skills add tw93/Waza -a claude-code -g -y"
  exit 1
fi
bash "$HEALTH_SCRIPT"

Sections may show (unavailable) when tools are missing:

• jq missing → conversation sections unavailable
• python3 missing → MCP/hooks/allowedTools sections unavailable
• settings.local.json absent → hooks/MCP may be unavailable (normal for global-only setups)

Treat (unavailable) as insufficient data, not a finding. Do not flag those areas.

The collector includes both runtime-specific and agent-agnostic surfaces:

• AGENT CONFIG SUMMARY / AGENT CONFIG DETAIL for Codex, Claude, Pi, and project instruction files.
• AI MAINTAINABILITY SUMMARY / AI MAINTAINABILITY DETAIL for project shape, verification surface, hotspot ownership, wrappers, and doc links.

Step 1b: MCP Live Check

Test every MCP server: call one harmless tool per server. Record live=yes/no with error detail. Respect enabled: false (skip without flagging). For API keys, only check if the env var is set (echo $VAR | head -c 5), never print full keys.

Step 1c: Safety and security checks

These run after collection and before the Step 2 analysis. The first two apply to every audit; the third only to projects with long-running or autonomous agents.

Security Baseline Checks

Run these on every audit, regardless of tier. They are the floor, not the ceiling.

Deny-list floor. Apply this only when the project or runtime exposes agent permission settings, hook settings, MCP settings, allowed/denied tools, or a documented autonomous-agent launcher. In that case, the settings should deny, at minimum: credential and key directories (SSH, cloud providers, GPG, gh CLI), secret files (.env, credentials*, secrets*), pipe-to-shell installers (curl ... | bash, wget ... | sh), and outbound shells (ssh, scp, nc). Report this as one concise WARN with the missing categories and suggested fix; let the reviewer fill in exact local paths from the environment. If no agent settings surface exists, report the deny-list as not applicable rather than a failure.

Environment override surface. Treat the following as attack surface, report when set in tracked files or shipped settings without a justification comment: API base-URL overrides (redirect all traffic to a third party), auto-trust flags for project-local MCP servers, wildcard tool allowlists (allowedTools: ["*"]), and permission-skip flags (--dangerously-skip-permissions or equivalents). Print file:line and the key name only; never print secrets.

Memory and Skill Supply Chain

Treat agent memory and third-party skills as supply-chain artifacts. They run with the user's privileges.

Memory hygiene. Audit the project's long-term agent memory store for secrets, tokens, or credentials (Critical), and for entries written by untrusted runs (subagent invoked on attacker-controlled input, /loop iteration over external content); recommend rotation after such runs. For high-risk one-off runs (untrusted PDFs, uncontrolled scraping, third-party scripts), recommend disabling memory persistence for that session entirely.

Skill supply chain. Third-party skills, plugins, and MCP servers run with the user's privileges. For each one not authored in this repo, check: source pinned to a release tag or revision (not main, a branch, or a remote git marketplace left tracking its latest head), hook handlers do not write to credential directories, MCP servers have explicit user consent (not auto-trusted by wildcard). Report unpinned sources or unreviewed hook handlers as Structural, not Critical, unless an active exploit signal is present.

Long-Running Agent Stop Conditions

For projects that use /loop, autonomous agents, or any long-running agent flow, the project must define explicit stop conditions. An agent that never stops is a budget and safety incident waiting to happen.

Audit for these four hard stop signals; flag the absence of each as a Structural finding:

1. No progress across two consecutive checkpoints. Same files touched, same errors logged, no new commits/tests/output. Recommend killing the loop and surfacing the state, not retrying.
2. Repeated identical failure. Same stack trace, same error message, same failed assertion three times in a row means the hypothesis is wrong; more attempts will not help.
3. Cost or token budget exceeded. Project should declare a per-run budget (tokens, API spend, wall-clock minutes). Loop exits when the budget is hit, not when work is done.
4. External blockers. Merge conflict on the target branch, dependency lock the agent cannot resolve, missing credential, network unreachable. Any of these halt the loop and ask the user, not retry forever.

The stop conditions should live in tracked project docs (AGENTS.md, the loop's launch script, or a dedicated config), not only in the agent's prompt. Prompts are forgettable; tracked config is enforceable. Recommend hooks (PostToolUse on the relevant tools) over prompt instructions when the project supports them: a hook physically cannot be skipped, a prompt instruction can. Confirm the host's hook coverage before recommending one: some agents only fire PostToolUse for a subset of tools (for example, a runtime may match shell/Bash only), so a fixup that must run after file edits belongs on a Stop or session-end hook there instead.

Step 2: Analyze

Confirm the tier. Then route:

• Simple: Analyze locally. No subagents.
• Standard: Analyze locally from the summary output. Do not launch subagents by default. If the user asks for a deep/full/thorough audit, or if local analysis cannot classify a security/control issue, escalate to deep mode and explain the likely token cost.
• Complex, remembered deep preference, explicit deep audit, or explicit AI maintainability audit: Re-run collection with bash "$HEALTH_SCRIPT" auto deep, then launch the relevant subagents in parallel. Redact credentials to [REDACTED].
  • Agent 1 (Context + Security): Read agents/inspector-context.md. Feed CONVERSATION SIGNALS section.
  • Agent 2 (Control + Behavior): Read agents/inspector-control.md. Feed detected tier.
  • Agent 3 (AI Maintainability): Read agents/inspector-maintainability.md. Feed only TIER METRICS, AI MAINTAINABILITY SUMMARY or AI MAINTAINABILITY DETAIL, and the script hotspot lists. Launch this agent only for deep health audits, Complex projects, or explicit code-rot/AI-maintainability requests.
• Fallback: If a subagent fails, analyze that layer locally and note "(analyzed locally)".

Step 3: Report

Health Report: {project} ({tier} tier, {file_count} files)

[PASS] Passing checks (table, max 5 rows)

Finding format

- [severity] <symptom> ({file}:{line} if known)
  Why: <one-line reason>
  Action: <exact command or edit to fix>

Action: must be copy-pasteable. Never write "investigate X" or "consider Y". If the fix is unknown, name the diagnostic command.

[!] Critical -- fix now

Rules violated, dangerous allowedTools, MCP overhead >12.5%, security findings, leaked credentials.

Example:

• [!] settings.local.json committed to git (exposes MCP tokens) Why: leaked token enables remote code execution via installed MCP servers Action: git rm --cached .claude/settings.local.json && echo '.claude/settings.local.json' >> .gitignore

[~] Structural -- fix soon

Agent instructions in the wrong layer, missing hooks, oversized descriptions, verifier gaps.

Codex/Claude/Pi instruction drift. Use AGENT CONFIG SUMMARY first. Report a Structural finding when AGENTS.md and runtime-specific files both contain substantial guidance without delegation, when Codex config.toml lacks trust for the current project, when Pi settings or package metadata point at missing skill roots, when project agent instructions are missing, or when runtime-specific instructions contradict the shared project source of truth. Also report when important rules live only in ignored or private local instruction overlays but the tracked/public docs lack them; those overlays are private context, not durable project source of truth. Do not print raw config values. Secrets, tokens, keys, and passwords must appear only as [REDACTED].

Quick check from the project root:

bash skills/health/scripts/check-agent-context.sh . summary

AI-maintainability gaps. Use AI MAINTAINABILITY SUMMARY in summary mode and AI MAINTAINABILITY DETAIL in deep mode. Report FAIL when the project has no executable verification command, no agent instruction surface for a non-trivial repo, or broken doc references. Report WARN when instructions exist but lack a project map, verification guidance, boundary/non-goal language, when TODO/HACK markers are concentrated, when large source hotspots lack ownership/boundary and verification guidance, or when durable docs contain raw one-off review reports, scorecards, dated line references, or diagnostic dumps instead of stable invariants. Treat missing docs/, specs/, .specify/, HANDOFF.md, CHANGELOG, issue templates, and PR templates as informational unless project complexity makes them necessary for handoff. The action for stale reports is to extract stable rules into public instructions, rules, references, or verifier scripts, then remove or archive the transient report.

Conversation-derived guidance. When a health audit reads recent agent conversations, do not recommend copying the conversation or a scorecard into docs. Recommend a candidate-matrix pass instead:

Field	Question
Repeated failure	Did this recur across fixes, releases, agents, or user reports?
Durable invariant	Can the lesson be stated as a stable rule, not a dated incident summary?
Target layer	Should it live in project instructions, a Waza skill, a global rule, or private memory?
Verifier	Is there a deterministic command, script, artifact check, or runtime smoke that can enforce it?
Redaction risk	Does the lesson require local paths, issue numbers, customer details, machine state, secrets, or unpublished release facts?

Layering rule: project-specific commands, app names, artifact names, and release rituals stay in the project; reusable workflows such as cancelled-release review gates or native-freeze evidence ladders belong in Waza skills; universal honesty and verification rules belong in global CLAUDE/AGENTS; private user preferences and one-machine facts stay in memory. If the lesson cannot pass the redaction-risk field, keep it out of public guidance.

Concentrated fix chains. Run git log --oneline --since='2 weeks ago' | grep -i fix and group by area (the prefix before : or (). When the same area has 3+ fix commits in a short window, it signals a missing structural invariant: each fix is a guess at a rule that was never written down. Report a Structural WARN with the area name, fix count, and recommend adding an explicit rule to AGENTS.md / CLAUDE.md / project rules that captures the invariant those fixes were converging toward. A concentrated fix chain that touches the same file 4+ times is a stronger signal than scattered fixes across different files.

Hotspot ownership gaps. In deep mode, read HOTSPOT OWNERSHIP SURFACE. If a largest source file exceeds the hotspot threshold and AGENTS.md / CLAUDE.md / shared instruction files do not name who owns the hotspot, what boundary should stay stable, and which verification command covers it, report a Structural WARN. Do not treat documented large files as code rot by size alone; some modules are intentionally large.

Missing stable verifier wrapper. If the repo exposes multiple verification commands through CI, scripts, or manifests but Makefile has no check, test, or verify target, report a Structural WARN. This is an AI-maintainability gap because agents need one stable default entrypoint, not because the project is broken.

Quick check from the project root:

bash skills/health/scripts/check-maintainability.sh . summary

For deep audits:

bash skills/health/scripts/check-maintainability.sh . deep

Keep actions concrete and non-invasive: add or fix the smallest useful instruction surface, add one executable validation command, document hotspot ownership and tests, split only when the boundary is already clear, or repair the broken reference. Do not propose broad rewrites from the script output alone.

Broken doc references. Scan AGENTS.md, CLAUDE.md, .claude/rules/*.md, and every .claude/skills/*/SKILL.md for references shaped like @<path>, ~/.claude/rules/<name>.md, ~/.claude/skills/<name>/, docs/<name>.md, or references/<name>.md. For each match, check that the target exists on disk. Report every "referenced but missing" pointer with the source file and line.

Common offenders:

• A project-level rule references a global rule file that was never created (e.g. ~/.claude/rules/swift.md).
• A CLAUDE.md uses an @AGENTS.md placeholder but the actual AGENTS.md is missing or empty.
• A skill body references references/<name>.md but only references/<name>-v2.md exists.
• A rule file references a deleted skill path.

Quick check from the project root:

bash skills/health/scripts/check-doc-refs.sh .

The checker resolves @... and docs/... from the project root, expands ~, resolves references/... from each .claude/skills/<name>/SKILL.md directory, checks every reference on a line, skips fenced code examples, and exits non-zero when any target is missing.

Report missing references as Structural findings, not Critical, unless the missing file is named as a hard dependency (e.g. release.md for the project's release skill).

Broken Markdown references. In deep mode, check-maintainability.sh also scans repository Markdown links. Report these as Structural findings when they point to missing local files, especially design, security, release, or handoff docs that agents may follow during future work.

Stale verifier cache output. If validation output points at a deleted temp worktree or non-existent /tmp / /private/tmp file, parse the captured log with:

bash skills/health/scripts/check-verifier-output.sh . <log-file>

Only use this script for existing command output supplied by the user or generated during the current audit. Do not run project tests just to feed this checker. Known actions include golangci-lint cache clean, go clean -cache -testcache, and npm cache verify; unknown tools get a diagnostic rerun action.

[-] Incremental -- nice to have

Outdated items, global vs local placement, context hygiene, stale allowedTools entries.

---

If no issues: All relevant checks passed. Nothing to fix.

Non-goals

• Never auto-apply fixes without confirmation.
• Never apply complex-tier checks to simple projects.
• Never act as a heavy lint, typecheck, duplication, or architecture-rewrite substitute; /health reports maintainability guardrails and concrete next actions only.

Gotchas

What happened	Rule
Missed the local override	Always read settings.local.json too; it shadows the committed file
Subagent timeout reported as MCP failure	MCP failures come from the live probe, not data collection
Reported issues in wrong language	Honor CLAUDE.md Communication rule first
Flagged intentionally noisy hook as broken	Ask before calling a hook "broken"
Hook seemed not to fire, but it did -- a later UI element rendered above it	Hook firing order is not visual order. Before re-editing the hook config: (a) confirm with --debug or by piping output, (b) check whether a diff dialog, permission prompt, or other UI element rendered on top and pushed the hook output offscreen, (c) only then suspect the hook itself.
/health burned too much quota on first run	Stay in summary mode first. Full conversation extracts and inspector subagents are deep-audit tools, not the default path for Standard projects.
Treated missing specs/docs as a failure	Decision artifacts are optional by default. Escalate missing docs/specs only when the tier, active handoff risk, or user request makes them necessary.
Treated an ignored AGENTS/CLAUDE file as durable project truth	Report whether the rule is tracked and distributed. Local overlays can inform the audit, but durable fixes belong in public repo docs or shipped skill/rule files.
Treated a review scorecard as maintainability documentation	Scorecards are snapshots. Extract the invariant and verification path, then remove or archive the report instead of calling the score itself a durable rule.