runbook-generator（运行手册生成器）

Runbook Generator

Tier: POWERFUL Category: Engineering / SRE Maintainer: Claude Skills Team

Overview

Analyze a codebase and generate production-grade operational runbooks with copy-paste commands, verification checks after every step, rollback procedures for every destructive action, escalation paths with contact information, and time estimates for capacity planning. Detects the stack (CI/CD, database, hosting, containers) and produces runbooks tailored to the actual infrastructure. Includes staleness detection to flag runbooks when referenced config files change.

Keywords

runbook, operational procedures, incident response, deployment, rollback, database maintenance, scaling, monitoring, on-call, SRE, postmortem

Core Capabilities

1. Stack Detection

• Identify CI/CD platform, database, hosting, and orchestration from repo files
• Map detected stack to appropriate runbook templates
• Extract connection strings, deployment commands, and infrastructure details

2. Runbook Types

• Deployment: pre-checks, deploy steps, smoke tests, rollback
• Incident response: triage, diagnose, mitigate, resolve, postmortem
• Database maintenance: backup, migration, vacuum, reindex
• Scaling: horizontal and vertical scaling procedures
• Monitoring: alert setup, dashboard configuration, on-call rotation

3. Format Discipline

• Numbered steps with copy-paste commands
• Verification check after EVERY step
• Time estimates for capacity planning
• Rollback procedure for every destructive action
• Escalation paths with decision criteria

4. Maintenance

• Staleness detection linked to config file modification dates
• Quarterly review cadence
• Staging dry-run validation framework

When to Use

• Codebase has no runbooks and you need to bootstrap them
• Existing runbooks are outdated or incomplete
• Onboarding a new engineer for on-call rotation
• Preparing for an incident response drill
• Post-incident improvement: updating runbooks with lessons learned

Stack Detection

Scan the repository before writing any runbook:

# CI/CD Platform
[ -d ".github/workflows" ] && echo "GitHub Actions"
[ -f ".gitlab-ci.yml" ]    && echo "GitLab CI"
[ -f "Jenkinsfile" ]       && echo "Jenkins"

# Database
grep -rl "postgres\|postgresql" package.json pyproject.toml 2>/dev/null && echo "PostgreSQL"
grep -rl "mysql\|mariadb" package.json 2>/dev/null && echo "MySQL"
grep -rl "mongodb\|mongoose" package.json 2>/dev/null && echo "MongoDB"

# Hosting
[ -f "vercel.json" ]       && echo "Vercel"
[ -f "fly.toml" ]          && echo "Fly.io"
[ -f "railway.toml" ]      && echo "Railway"
[ -d "terraform" ]         && echo "Terraform (custom cloud)"
[ -d "k8s" ] || [ -d "kubernetes" ] && echo "Kubernetes"
[ -f "docker-compose.yml" ] && echo "Docker Compose"

# Framework
[ -f "next.config.mjs" ] || [ -f "next.config.ts" ] && echo "Next.js"
grep -q "fastapi" requirements.txt 2>/dev/null && echo "FastAPI"
[ -f "go.mod" ] && echo "Go"

Deployment Runbook Template

# Deployment Runbook — [App Name]

**Stack:** [Framework] + [Database] + [Hosting]
**Last verified:** YYYY-MM-DD
**Owner:** [Team Name]
**Estimated total time:** 15-25 minutes

---

## Staleness Check

| Config File | Last Modified | Affects Steps |
|-------------|--------------|---------------|
| vercel.json | `git log -1 --format=%ci -- vercel.json` | Deploy, Rollback |
| db/schema.ts | `git log -1 --format=%ci -- db/schema.ts` | Migration |
| .github/workflows/deploy.yml | `git log -1 --format=%ci -- .github/workflows/deploy.yml` | CI |

If any config was modified after "Last verified" date, review affected steps.

---

## Pre-Deployment Checklist
- [ ] All PRs merged to main
- [ ] CI passing on main branch
- [ ] Database migrations tested in staging
- [ ] Rollback plan confirmed
- [ ] On-call engineer notified

## Step 1: Verify CI Status (2 min)

Check latest CI run

gh run list --branch main --limit 3

Verify specific run

gh run view <run-id>

VERIFY: Latest run shows green checkmark. If red, do not proceed.

## Step 2: Apply Database Migrations (5 min)

Staging first

DATABASE_URL=$STAGING_DB_URL pnpm db:migrate

Verify migration applied

DATABASE_URL=$STAGING_DB_URL pnpm db:migrate status

VERIFY: Output shows "All migrations applied" with today's date.

Production (only after staging verification)

DATABASE_URL=$PROD_DB_URL pnpm db:migrate

VERIFY: Same output as staging. If error, see Rollback section.

WARNING: For migrations on tables with >1M rows, schedule during low-traffic window and monitor lock wait times.

## Step 3: Deploy to Production (5 min)

Option A: Git push triggers deployment

git push origin main

Option B: Manual trigger

vercel --prod

or: fly deploy

or: kubectl apply -f k8s/deployment.yaml

VERIFY: Deployment dashboard shows new version in progress. Note the deployment URL/ID for rollback.

## Step 4: Smoke Test (5 min)

Health check

curl -sf https://myapp.com/api/health | jq .

Critical user path

curl -sf https://myapp.com/api/v1/me \ -H "Authorization: Bearer $TEST_TOKEN" | jq '.id'

Check error rate (wait 2 minutes for data)

Dashboard: [link to monitoring dashboard]

VERIFY:

- Health returns `{"status": "ok", "db": "connected"}`
- User endpoint returns a valid user ID
- Error rate < 1% on monitoring dashboard

## Step 5: Monitor (10 min)

Watch these metrics for 10 minutes after deployment:

- Error rate: < 1% (dashboard: [link])
- P95 latency: < 200ms (dashboard: [link])
- Active DB connections: < 80% of max (query below)

psql $PROD_DB_URL -c "SELECT count(*) FROM pg_stat_activity;"

VERIFY: All metrics within normal range. If any spike, proceed to Rollback.

---

## Rollback

If smoke tests fail or metrics degrade:

Instant rollback via Vercel

vercel rollback [previous-deployment-url]

or Fly.io

fly releases --app myapp fly deploy --image [previous-image]

or Kubernetes

kubectl rollout undo deployment/myapp

Database rollback (ONLY if migration was applied in this deploy)

DATABASE_URL=$PROD_DB_URL pnpm db:rollback

VERIFY: Previous version serving traffic. Run smoke tests again.

---

## Escalation

| Level | Who | When | Contact |
| --- | --- | --- | --- |
| L1 | On-call engineer | First responder | PagerDuty rotation |
| L2 | Platform lead | DB issues, rollback failures | Slack: @platform-lead |
| L3 | VP Engineering | Production down > 30 min | Phone: [number] |

Incident Response Runbook Template

# Incident Response Runbook

**Severity:** P1 (down), P2 (degraded), P3 (minor)
**Estimated time:** P1: 30-60 min, P2: 1-4 hours, P3: next business day

---

## Phase 1: Triage (5 min)

### Confirm the Incident

Is the app responding?

curl -sw "%{http_code}" https://myapp.com/api/health -o /dev/null

Check for errors in recent logs

vercel logs --since=15m | grep -i "error\|exception\|5[0-9][0-9]"

or: kubectl logs -l app=myapp --since=15m | grep -i error

VERIFY: 200 = app is up. 5xx or timeout = incident confirmed.

### Declare Severity

| Condition | Severity | Action |
| --- | --- | --- |
| Site completely unreachable | P1 | Page L2/L3 immediately |
| Partial degradation or slow | P2 | Notify team channel |
| Single feature broken | P3 | Create ticket, fix in business hours |

### Communicate

Post to incident channel (adjust for your tool)

Slack: #incidents "INCIDENT: [severity] — [brief description]. Investigating. Updates every 15 min."

## Phase 2: Diagnose (10-15 min)

### Check Recent Changes

Was something just deployed?

vercel ls --limit 5

or: kubectl rollout history deployment/myapp

Recent commits

git log --oneline -10

### Check Database

Active queries (look for long-running or blocked queries)

psql $PROD_DB_URL -c " SELECT pid, now() - query_start AS duration, state, query FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC LIMIT 20;"

Connection pool saturation

psql $PROD_DB_URL -c " SELECT count(*) AS active, (SELECT setting::int FROM pg_settings WHERE name = 'max_connections') AS max FROM pg_stat_activity;"

### Diagnostic Decision Tree

Recent deploy + new errors → ROLLBACK (see Deployment Runbook) DB queries hanging → Kill long queries, check connection pool External API failing → Check status pages, enable circuit breaker Memory/CPU spike → Check for infinite loops, scale up temporarily

## Phase 3: Mitigate (variable)

Kill a runaway database query

psql $PROD_DB_URL -c "SELECT pg_terminate_backend(<pid>);"

Rollback last deployment

vercel rollback [previous-url]

Scale up (if capacity issue)

fly scale count 4 --app myapp

or: kubectl scale deployment/myapp --replicas=4

## Phase 4: Resolve and Postmortem

Within 24 hours of resolution:

1. Write incident timeline (what happened, when, who noticed, what fixed it)
2. Identify root cause (5 Whys analysis)
3. Define action items with owners and due dates
4. Update this runbook if a step was missing or wrong
5. Add monitoring/alerting that would have caught this earlier

Database Maintenance Runbook Template

# Database Maintenance — PostgreSQL

**Schedule:** Weekly vacuum (automated), monthly manual review

## Backup

pg_dump $PROD_DB_URL \ --format=custom \ --compress=9 \ --file="backup-$(date +%Y%m%d-%H%M%S).dump"

VERIFY: File exists and size > 0. Test monthly with:

pg_restore --dbname=$STAGING_DB_URL backup-*.dump psql $STAGING_DB_URL -c "SELECT count(*) FROM users;"

## Vacuum and Reindex

Check bloat

psql $PROD_DB_URL -c " SELECT tablename, pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size, n_dead_tup, ROUND(n_dead_tup::numeric / NULLIF(n_live_tup, 0) * 100, 1) AS dead_pct FROM pg_stat_user_tables ORDER BY n_dead_tup DESC LIMIT 10;"

Vacuum high-bloat tables (non-blocking)

psql $PROD_DB_URL -c "VACUUM ANALYZE tablename;"

Reindex (CONCURRENTLY to avoid locks)

psql $PROD_DB_URL -c "REINDEX INDEX CONCURRENTLY index_name;"

VERIFY: dead_pct drops below 5% after vacuum.

Staleness Detection Automation

#!/bin/bash
# check-runbook-staleness.sh
# Run weekly in CI to detect stale runbooks

RUNBOOK_DIR="docs/runbooks"
EXIT_CODE=0

for runbook in "$RUNBOOK_DIR"/*.md; do
  LAST_VERIFIED=$(grep -oP 'Last verified:\s*\K\d{4}-\d{2}-\d{2}' "$runbook" 2>/dev/null)
  if [ -z "$LAST_VERIFIED" ]; then
    echo "WARNING: $runbook has no 'Last verified' date"
    continue
  fi

  # Extract referenced config files
  CONFIG_FILES=$(grep -oP 'git log.*-- \K[^\x60]+' "$runbook" 2>/dev/null)
  for config in $CONFIG_FILES; do
    if [ -f "$config" ]; then
      LAST_MODIFIED=$(git log -1 --format=%ci -- "$config" | cut -d' ' -f1)
      if [[ "$LAST_MODIFIED" > "$LAST_VERIFIED" ]]; then
        echo "STALE: $runbook references $config (modified $LAST_MODIFIED, verified $LAST_VERIFIED)"
        EXIT_CODE=1
      fi
    fi
  done
done

exit $EXIT_CODE

Quarterly Review Process

Every quarter (add to team calendar):

1. Run each command in staging — does it still work?
2. Check config drift — compare config modification dates vs runbook verification date
3. Test rollback procedures — actually roll back in staging
4. Update contact info — L1/L2/L3 assignments may have changed
5. Add new failure modes discovered in the past quarter
6. Update "Last verified" date at the top of each reviewed runbook
7. Archive obsolete runbooks — services get decommissioned

Common Pitfalls

Best Practices

1. Every command must be copy-pasteable — use env vars, not placeholder text
2. VERIFY after every step — explicit expected output, not "it should work"
3. Time estimates are mandatory — engineers need to know if they have time before SLA breach
4. Rollback before you deploy — plan the undo before executing the action
5. Runbooks live in the repo — docs/runbooks/, versioned with the code they describe
6. Postmortem drives runbook updates — every incident should improve at least one runbook
7. Link, do not duplicate — reference the canonical config, do not copy its contents
8. Test runbooks like you test code — untested runbooks are worse than no runbooks (false confidence)

Troubleshooting

Success Criteria

• Runbook coverage >= 90% — every production service has at least a deployment and incident response runbook
• Mean time to mitigate (MTTM) drops by 30%+ within one quarter of adopting generated runbooks
• Zero placeholder commands — every command in every runbook is copy-pasteable without manual editing beyond env var substitution
• Staleness rate < 10% — fewer than 10% of runbooks flagged as stale in any given quarterly review cycle
• Quarterly dry-run pass rate >= 95% — at least 95% of runbook steps execute successfully in staging during scheduled dry-runs
• On-call onboarding time < 2 hours — a new engineer can read all runbooks for their service and feel confident to handle L1 incidents within two hours
• Post-incident runbook update rate = 100% — every postmortem produces at least one runbook addition or correction

Scope & Limitations

This skill covers:

• Generating deployment, incident response, database maintenance, scaling, and monitoring runbooks from codebase analysis
• Stack detection for common CI/CD platforms (GitHub Actions, GitLab CI, Jenkins), databases (PostgreSQL, MySQL, MongoDB), and hosting providers (Vercel, Fly.io, Kubernetes, AWS)
• Staleness detection automation and quarterly review processes
• Escalation path templates with severity-based routing

This skill does NOT cover:

• Automated execution of runbook steps — it generates documentation, not orchestration (see ci-cd-pipeline-builder for automated pipelines)
• Infrastructure provisioning or Terraform/Pulumi code generation (see migration-architect for schema migration tooling)
• Observability stack setup such as Prometheus rules, Grafana dashboards, or alert definitions (see observability-designer for monitoring infrastructure)
• Security incident response or vulnerability remediation playbooks (see skill-security-auditor for security-focused analysis)

Integration Points