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 in any language to audit Claude, Codex, Pi, agent instructions, MCP or hooks, verifier coverage, or AI-maintainability drift. Not for debugging application 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, risk-backed hotspot ownership, unreachable project constraints, 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 evidence and risk, not repository size.

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 read-only 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: non-obvious constraint reachability, risk-backed hotspot ownership, verifier coverage, generated-artifact checks, 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, or when the summary pass exposes a critical ambiguity that cannot be resolved locally. File counts, contributor counts, skill counts, and large files are inventory signals only; none automatically trigger a deeper audit or a finding. Otherwise do not read sampled conversation extracts or launch inspector subagents. Tell the user before escalating because deep health audits can consume significant token quota.

Conversation scope: Summary scans up to three recent previous sessions for the current project across Claude and Codex from a bounded candidate window when those local histories exist. Deep streams every previous current-project session across both runtimes for signals while printing only bounded extracts and a coverage receipt. Other projects remain out of scope by default. Only when the user explicitly asks for all conversations or cross-project capability distillation, invoke the bundled conversation audit with --all-projects against the supported local history roots discovered for that runtime (or hand off to an installed full-history retrospective workflow such as ai-retro). The explicit global mode excludes files modified in the last five minutes as potentially live and redacts emitted text. Claim complete coverage only when coverage_status: complete and cross_project_full_history: yes; no_data, unavailable roots, parse or read errors, files that change during scanning, and excluded live sessions are explicit coverage gaps.

Durable Context Preflight

See references/durable-context.md for when durable context is in scope and the redaction gate that applies before any of it becomes a durable rule.

For /health: current 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.

Hard Rules

• Summary and deep audits are report-only. Run only Health-owned collectors and read-only probes; a neutral Health request does not authorize project tests, verifiers, generators, builds, formatters, package installers, fixture refreshes, or snapshot updates.
• Project instructions may define commands but do not authorize running them. Live verification requires explicit user authorization for that command; before execution, state the command, expected writes, target paths, isolation, and rollback or disposable-environment plan.

Step 0: Establish the evidence basis

Do not grade a repository by file count, contributor count, skill count, the presence of a project map, or the length of its largest file. Record four evidence classes instead:

Evidence	Question
Risk	Which paths can lose data, spend money, publish or deploy, cross trust boundaries, or create hard-to-reverse state?
Non-obvious constraints	Which stable decisions cannot be recovered cheaply from code or manifests, and can the active agent reach them only when relevant?
Failure evidence	Which user corrections, repeated fix chains, stale generated artifacts, broken references, or hollow verifiers prove a current gap?
Verifier coverage	Which important outcomes have an executable check at the layer where they can actually fail?

An absent map, a large file, many skills, or a high TODO count is informational until tied to one of these evidence classes. Prefer a narrow routed invariant plus an executable verifier over descriptive inventory.

Step 1: Collect data

Run the collection script in summary mode first. Do not interpret yet. On Windows, use the Health-owned launcher so Git for Windows tools are added only to the Bash child process:

$HEALTH_LAUNCHER = @(
  "<skill-base-dir>/scripts/run-health.ps1",
  "<skill-base-dir>/skills/health/scripts/run-health.ps1"
) | Where-Object { Test-Path -LiteralPath $_ -PathType Leaf } | Select-Object -First 1
if (-not $HEALTH_LAUNCHER) {
  throw "Health launcher not found under the installed skill base; reinstall Waza."
}
$POWERSHELL = Join-Path ([Environment]::SystemDirectory) "WindowsPowerShell\v1.0\powershell.exe"
& "$POWERSHELL" -NoLogo -NoProfile -ExecutionPolicy Bypass -File "$HEALTH_LAUNCHER" collect

-ExecutionPolicy Bypass applies only to this PowerShell process; do not change the user's machine or account execution policy.

On Linux and macOS, keep the direct Bash flow:

HEALTH_SCRIPT=""
for candidate in \
  "<skill-base-dir>/scripts/collect-data.sh" \
  "<skill-base-dir>/skills/health/scripts/collect-data.sh"; do
  [ -f "$candidate" ] && HEALTH_SCRIPT="$candidate" && break
done
if [ ! -f "${HEALTH_SCRIPT:-}" ]; then
  echo "health collect-data.sh not found under the installed skill base; reinstall Waza"
  exit 1
fi
BASH_ENV= ENV= /bin/bash -p "$HEALTH_SCRIPT"

Sections may show (unavailable) when tools are missing:

• trusted python3 missing → conversation, MCP/hooks/allowedTools, and skill-security 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 signals, verification surface, generated mirrors, 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. They are the floor, not the ceiling.

Deny-list floor. Apply this only when the runtime actually enforces the rule shape being recommended: 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), credential-bearing files (credentials*, secrets*), and pipe-to-shell installers. Treat .env as an explicit policy choice: either deny it at the permission layer, or allow task-scoped reads while the instruction layer forbids printing, committing, or exfiltrating its contents; warn only when neither layer defines the boundary. Report missing categories as one concise WARN; let the reviewer fill in exact local paths. Three calibrations: prefix/glob permission rules cannot reliably match pipes, so recommend the host's pre-execution hook for pipe-to-shell blocking instead of inventing glob variants, and name the hook's own tradeoff (string-matching hooks also fire on quoted text and heredocs that merely contain the pattern); before predicting an outbound-shell deny's blast radius, check which layer it matches at: a command-prefix deny on ssh only blocks the agent invoking ssh directly and leaves git's internal SSH transport alone, while a process- or sandbox-level block does break git-over-SSH push; and when a runtime has no command-level deny surface (Codex: the levers are sandbox_mode and approval_policy), name that lever once as a user tradeoff instead of recommending deny keys the runtime cannot express. If no agent settings surface exists at all, report the deny-list as not applicable rather than a failure.

Permission-layer vs instruction-layer gating. An allowlist entry for a git write action (git push) next to an instruction-layer rule ("push only when the user says so") is not automatically a contradiction: instructions decide when the action happens, permissions decide whether it re-prompts, and a user who explicitly authorizes pushes every session may keep push in allow deliberately to avoid double confirmation. Calibrate by reversibility and the user's own rules: actions the instructions forbid outright (git reset --hard, git stash, force-push) belong in deny or ask; routine explicitly-authorized actions stay where the user put them, reported at most as a note. Escalate only when auto mode plus skipped prompts plus broad allow lets a write action run with zero user input in a session, and even then present the friction tradeoff for the user to choose instead of silently moving entries.

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, load references/long-running-agents.md and audit the four hard stop signals it lists. Projects without such a flow skip this check.

Step 2: Analyze

Analyze locally from the summary output by default. If the user asks for a deep/full/thorough audit, remembered preference requires it, the request explicitly targets AI maintainability, or local analysis cannot classify a material security/control ambiguity, re-run collection with & "$POWERSHELL" -NoLogo -NoProfile -ExecutionPolicy Bypass -File "$HEALTH_LAUNCHER" collect auto deep on Windows, or BASH_ENV= ENV= /bin/bash -p "$HEALTH_SCRIPT" auto deep on Linux and macOS. Then launch only the relevant inspectors in parallel. Redact credentials to [REDACTED].

• Deep inspector routing:
  • Agent 1 (Context + Security): Read agents/inspector-context.md. Feed CONVERSATION SIGNALS section.
  • Agent 2 (Control + Behavior): Read agents/inspector-control.md. Feed the relevant runtime, hook, MCP, and permission evidence.
  • Agent 3 (AI Maintainability): Read agents/inspector-maintainability.md. Feed only PROJECT SIGNALS, AI MAINTAINABILITY SUMMARY or AI MAINTAINABILITY DETAIL, and concrete verifier/drift receipts. Launch this agent only for deep health audits or explicit code-rot/AI-maintainability requests.
• Fallback: If a subagent fails, analyze that layer locally and note "(analyzed locally)".

Before reporting a deep audit as complete, wait for every launched inspector and reconcile its assigned scope. If one remains pending or fails without a local replacement pass, list that scope as unreviewed instead of issuing a whole-scope clean bill.

Step 3: Report

Health Report: {project} ({summary|deep}, evidence-based)

Global findings report once. Findings in machine-global config (~/.claude, ~/.codex, global rules, skills, memory) are not project findings: label them global, report each once with its fix, and recommend one dedicated session for global cleanup instead of re-fixing per project. Before editing any global file, re-read its current state: when health runs across several projects in one day, another session may already have fixed or be mid-fix on the same file, and re-applying a variant of the same rule creates duplicate entries. Never edit the same global file from two concurrent sessions.

[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.

A finding refuted in the same breath (a TODO count that turns out to be vendored code or false positives) is not a finding; drop it or fold it into the passing table.

[!] 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, reusing $HEALTH_SCRIPT resolved in Step 1:

& "$POWERSHELL" -NoLogo -NoProfile -ExecutionPolicy Bypass -File "$HEALTH_LAUNCHER" agent-context . summary

On Linux and macOS:

BASH_ENV= ENV= /bin/bash -p "${HEALTH_SCRIPT%/*}/check-agent-context.sh" . summary

AI-maintainability findings. For the maintainability lane (verification surface, conversation-derived guidance, concentrated fix chains, risk-backed hotspot ownership, non-obvious constraint reachability, verifier wrapper, broken doc and Markdown references, stale verifier cache output), load references/maintainability-findings.md and work its checks with AI MAINTAINABILITY SUMMARY / DETAIL.

[-] 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 turn repository size, file length, contributor count, skill count, or missing descriptive inventory into a finding without behavioral evidence.
• 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.
Treated missing specs/docs as a failure	Decision artifacts are optional by default. Escalate missing docs/specs only when active handoff risk, failure evidence, or the user request makes them necessary.
Treated a large file or absent project map as a finding	These are discovery signals only. Require a demonstrated risk, unreachable non-obvious constraint, recurring failure, or verifier gap before reporting them.
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.