agentleFS
me2reshme2resh/apexyard/.claude/skills/monitoring-audit/SKILL.md

monitoring-audit

Skill542 starsChanged 11 days agoMITView on GitHub

Observability audit — logging, error tracking, health endpoints, alerting, runbooks. Deep-dive for /launch-check monitoring.

We split this skill into the rules it gives an agent

14 rules. About 0 are common, 10 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈10

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check initialization: is the SDK configured with the correct DSN/API key (env var, not hardcoded)?

    line 29
  2. Distinctive:

    Check error boundaries (React ErrorBoundary, Vue errorHandler, global uncaughtException handler)

    line 30
  3. Distinctive:

    Check if source maps are uploaded to the error tracker for readable stack traces

    line 31
  4. Very specific:

    Check for a health check route (/health, /healthz, /api/health, /_health)

    line 35
  5. Distinctive:

    Check what the health endpoint verifies (just "alive"?

    line 36
  6. Distinctive:

    Check for a readiness probe separate from liveness (Kubernetes-style)

    line 37
  7. Distinctive:

    Check for a logging library (winston, pino, bunyan, python logging, structured logging)

    line 41
  8. Distinctive:

    Check if logs are structured (JSON) or unstructured (console.log with string concatenation)

    line 42
  9. Very specific:

    Check for appropriate log levels (error, warn, info, debug) — not everything at console.log

    line 43
  10. Distinctive:

    Check that sensitive data (passwords, tokens, PII) is NOT logged

    line 44
  11. Distinctive:

    Check for alerting configuration (CloudWatch Alarms, Datadog monitors, PagerDuty rules)

    line 48
  12. Very specific:

    Check for a runbook (docs/runbook.md, docs/incident-response.md, RUNBOOK.md)

    line 49
  13. Distinctive:

    Check for a status page configuration (Statuspage.io, Instatus, or self-hosted)

    line 50
  14. Very specific:

    After printing the findings table, persist via the shared audit-history lib so the monitoring trend across runs becomes legible.

    line 75

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Writing: read docs/skills/shared/writing-rule.md before you write a durable artifact.

/monitoring-audit — Observability & Incident Readiness

Deep-dive observability analysis. Checks that production issues will be detected, alerted on, and resolvable. Invoke when /launch-check's monitoring row shows WARN or FAIL.

Observability Pillars

PillarQuestionWhat to look for
LogsCan you see what happened?Structured logging, log levels, no PII in logs
MetricsCan you measure what's happening?Request counts, latency, error rates, business metrics
TracesCan you follow a request end-to-end?Distributed tracing, correlation IDs
AlertsWill you know when something breaks?Alerting rules, on-call, PagerDuty/OpsGenie

Process

Step 1: Error tracking

  • Grep for error tracking SDK (Sentry, Datadog, Bugsnag, LogRocket, Rollbar)
  • Check initialization: is the SDK configured with the correct DSN/API key (env var, not hardcoded)?
  • Check error boundaries (React ErrorBoundary, Vue errorHandler, global uncaughtException handler)
  • Check if source maps are uploaded to the error tracker for readable stack traces

Step 2: Health endpoints

  • Check for a health check route (/health, /healthz, /api/health, /_health)
  • Check what the health endpoint verifies (just "alive"? database connectivity? external service reachability?)
  • Check for a readiness probe separate from liveness (Kubernetes-style)

Step 3: Logging

  • Check for a logging library (winston, pino, bunyan, python logging, structured logging)
  • Check if logs are structured (JSON) or unstructured (console.log with string concatenation)
  • Check for appropriate log levels (error, warn, info, debug) — not everything at console.log
  • Check that sensitive data (passwords, tokens, PII) is NOT logged

Step 4: Alerting and incident response

  • Check for alerting configuration (CloudWatch Alarms, Datadog monitors, PagerDuty rules)
  • Check for a runbook (docs/runbook.md, docs/incident-response.md, RUNBOOK.md)
  • Check for a status page configuration (Statuspage.io, Instatus, or self-hosted)

Step 5: Output

MONITORING AUDIT — <project> @ <sha>

| # | Pillar | Status | Finding |
|----|--------|--------|---------|
| M1 | Error tracking | PASS | Sentry configured in lib/sentry.ts, source maps uploaded |
| M2 | Health endpoint | FAIL | No /health route found |
| M3 | Logging | WARN | Using console.log (unstructured), no log levels |
| M4 | Alerting | WARN | No alerting rules found in code or IaC |
| M5 | Runbook | FAIL | No runbook or incident response doc |

Incident readiness: NOT READY (2 fails — health endpoint and runbook are minimum viable)

Priority fixes:
  1. [ ] Add GET /health returning { status: "ok", db: "connected" }
  2. [ ] Create docs/runbook.md with deployment, rollback, and common-issue procedures
  3. [ ] Replace console.log with structured logger (pino recommended for Node.js)

Persist the run + render trend

After printing the findings table, persist via the shared audit-history lib so the monitoring trend across runs becomes legible. See docs/agdr/AgDR-0019-audit-artefact-persistence.md.

Resolve project name + score + verdict

<project-name> from apexyard.projects.yaml (or basename + /handover reminder if unregistered).

Score: score = max(0, 100 - 25*critical - 10*high - 3*medium - 1*low). Verdict by worst-severity: critical/high → fail, medium → conditional, low/none → pass. Legacy "Incident readiness" two-state: NOT READY → fail, READY → pass.

Persist + render

source "$(git rev-parse --show-toplevel)/.claude/hooks/_lib-audit-history.sh"

# Lowercase severity in the payload — the lib expects critical/high/medium/low/info.
payload=$(mktemp); cat > "$payload" <<'EOF'
{
  "schema_version": 1,
  "findings": [
    {"id": "M2", "severity": "critical", "status": "open", "summary": "No error tracking SDK detected; uncaught errors are silent"},
    {"id": "M3", "severity": "high",     "status": "open", "summary": "No /health endpoint; load balancer can't detect hung instance"},
    {"id": "M4", "severity": "critical", "status": "open", "summary": "No alerting rules; nothing pages on-call"}
  ]
}
EOF

# Body: per templates/audits/monitoring-audit.md
body=$(mktemp); cat > "$body" <<'EOF'
... (filled-in body — findings table + Priority fixes) ...
EOF

ts=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
audit_run_persist "<project-name>" "monitoring-audit" "$ts" "fail" 35 "$body" < "$payload"
rm -f "$payload" "$body"

audit_render_trend "<project-name>" "monitoring-audit" 5

Opt-in commit

touch projects/<name>/audits/monitoring-audit/.audit-history-tracked

Rules

  1. Auto-PASS for projects not yet in production. Pre-launch monitoring gaps are expected — flag them as "set up before first deploy" rather than "FAIL."
  2. Check IaC too. Alerting rules might be in Terraform, CloudFormation, or CDK files rather than application code.
  3. Don't require all four pillars. Logs + error tracking + health endpoint is the minimum. Distributed tracing and detailed metrics are "nice to have" for most teams.
  4. Be specific about what the health endpoint should check — "returns 200" is table stakes; checking database connectivity is the real test.
  5. Always persist via the lib. The persist step runs regardless of opt-in commit state.
  6. Severity vocabulary in the JSON is lowercase. The lib expects critical/high/medium/low/info.

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