Runbook Generator

Tier: POWERFUL
Category: Engineering
Domain: DevOps / Site Reliability Engineering

Overview

Analyze a codebase and generate production-grade operational runbooks. Detects your stack (CI/CD, database, hosting, containers), then produces step-by-step runbooks with copy-paste commands, verification checks, rollback procedures, escalation paths, and time estimates. Keeps runbooks fresh with staleness detection linked to config file modification dates.

Core Capabilities

• Stack detection — auto-identify CI/CD, database, hosting, orchestration from repo files
• Runbook types — deployment, incident response, database maintenance, scaling, monitoring setup
• Format discipline — numbered steps, copy-paste commands, ✅ verification checks, time estimates
• Escalation paths — L1 → L2 → L3 with contact info and decision criteria
• Rollback procedures — every deployment step has a corresponding undo
• Staleness detection — runbook sections reference config files; flag when source changes
• Testing methodology — dry-run framework for staging validation, quarterly review cadence

When to Use

Use when:

• A codebase has no runbooks and you need to bootstrap them fast
• Existing runbooks are outdated or incomplete (point at the repo, regenerate)
• Onboarding a new engineer who needs clear operational procedures
• Preparing for an incident response drill or audit
• Setting up monitoring and on-call rotation from scratch

Skip when:

• The system is too early-stage to have stable operational patterns
• Runbooks already exist and only need minor updates (edit directly)

Stack Detection

When given a repo, scan for these signals before writing a single runbook line:

# CI/CD
ls .github/workflows/     → GitHub Actions
ls .gitlab-ci.yml         → GitLab CI
ls Jenkinsfile            → Jenkins
ls .circleci/             → CircleCI
ls bitbucket-pipelines.yml → Bitbucket Pipelines

# Database
grep -r "postgresql\|postgres\|pg" package.json pyproject.toml → PostgreSQL
grep -r "mysql\|mariadb"           package.json               → MySQL
grep -r "mongodb\|mongoose"        package.json               → MongoDB
grep -r "redis"                    package.json               → Redis
ls prisma/schema.prisma            → Prisma ORM (check provider field)
ls drizzle.config.*                → Drizzle ORM

# Hosting
ls vercel.json                     → Vercel
ls railway.toml                    → Railway
ls fly.toml                        → Fly.io
ls .ebextensions/                  → AWS Elastic Beanstalk
ls terraform/  ls *.tf             → Custom AWS/GCP/Azure (check provider)
ls kubernetes/ ls k8s/             → Kubernetes
ls docker-compose.yml              → Docker Compose

# Framework
ls next.config.*                   → Next.js
ls nuxt.config.*                   → Nuxt
ls svelte.config.*                 → SvelteKit
cat package.json | jq '.scripts'   → Check build/start commands

Map detected stack → runbook templates. A Next.js + PostgreSQL + Vercel + GitHub Actions repo needs:

• Deployment runbook (Vercel + GitHub Actions)
• Database runbook (PostgreSQL backup, migration, vacuum)
• Incident response (with Vercel logs + pg query debugging)
• Monitoring setup (Vercel Analytics, pg_stat, alerting)

Runbook Types

1. Deployment Runbook

# Deployment Runbook — [App Name]
**Stack:** Next.js 14 + PostgreSQL 15 + Vercel  
**Last verified:** 2025-03-01  
**Source configs:** vercel.json (modified: git log -1 --format=%ci -- vercel.json)  
**Owner:** Platform Team  
**Est. total time:** 15–25 min  

---

## Pre-deployment Checklist
- [ ] All PRs merged to main
- [ ] CI passing on main (GitHub Actions green)
- [ ] Database migrations tested in staging
- [ ] Rollback plan confirmed

## Steps

### Step 1 — Run CI checks locally (3 min)
```bash
pnpm test
pnpm lint
pnpm build

✅ Expected: All pass with 0 errors. Build output in .next/

Step 2 — Apply database migrations (5 min)

# Staging first
DATABASE_URL=$STAGING_DATABASE_URL npx prisma migrate deploy

✅ Expected: All migrations have been successfully applied.

# Verify migration applied
psql $STAGING_DATABASE_URL -c "\d" | grep -i migration

✅ Expected: Migration table shows new entry with today's date

Step 3 — Deploy to production (5 min)

git push origin main
# OR trigger manually:
vercel --prod

✅ Expected: Vercel dashboard shows deployment in progress. URL format: https://app-name-<hash>-team.vercel.app

Step 4 — Smoke test production (5 min)

# Health check
curl -sf https://your-app.vercel.app/api/health | jq .

# Critical path
curl -sf https://your-app.vercel.app/api/users/me \
  -H "Authorization: Bearer $TEST_TOKEN" | jq '.id'

✅ Expected: health returns {"status":"ok","db":"connected"}. Users API returns valid ID.

Step 5 — Monitor for 10 min

• Check Vercel Functions log for errors: vercel logs --since=10m
• Check error rate in Vercel Analytics: < 1% 5xx
• Check DB connection pool: SELECT count(*) FROM pg_stat_activity; (< 80% of max_connections)

Rollback

If smoke tests fail or error rate spikes:

# Instant rollback via Vercel (preferred — < 30 sec)
vercel rollback [previous-deployment-url]

# Database rollback (only if migration was applied)
DATABASE_URL=$PROD_DATABASE_URL npx prisma migrate reset --skip-seed
# WARNING: This resets to previous migration. Confirm data impact first.

✅ Expected after rollback: Previous deployment URL becomes active. Verify with smoke test.

Escalation

• L1 (on-call engineer): Check Vercel logs, run smoke tests, attempt rollback
• L2 (platform lead): DB issues, data loss risk, rollback failed — Slack: @platform-lead
• L3 (CTO): Production down > 30 min, data breach — PagerDuty: #critical-incidents

---

### 2. Incident Response Runbook

```markdown
# Incident Response Runbook
**Severity levels:** P1 (down), P2 (degraded), P3 (minor)  
**Est. total time:** P1: 30–60 min, P2: 1–4 hours  

## Phase 1 — Triage (5 min)

### Confirm the incident
```bash
# Is the app responding?
curl -sw "%{http_code}" https://your-app.vercel.app/api/health -o /dev/null

# Check Vercel function errors (last 15 min)
vercel logs --since=15m | grep -i "error\|exception\|5[0-9][0-9]"

✅ 200 = app up. 5xx or timeout = incident confirmed.

Declare severity:

• Site completely down → P1 — page L2/L3 immediately
• Partial degradation / slow responses → P2 — notify team channel
• Single feature broken → P3 — create ticket, fix in business hours

Phase 2 — Diagnose (10–15 min)

# Recent deployments — did something just ship?
vercel ls --limit=5

# Database health
psql $DATABASE_URL -c "SELECT pid, state, wait_event, query FROM pg_stat_activity WHERE state != 'idle' LIMIT 20;"

# Long-running queries (> 30 sec)
psql $DATABASE_URL -c "SELECT pid, now() - pg_stat_activity.query_start AS duration, query FROM pg_stat_activity WHERE state = 'active' AND now() - pg_stat_activity.query_start > interval '30 seconds';"

# Connection pool saturation
psql $DATABASE_URL -c "SELECT count(*), max_conn FROM pg_stat_activity, (SELECT setting::int AS max_conn FROM pg_settings WHERE name='max_connections') t GROUP BY max_conn;"

Diagnostic decision tree:

• Recent deploy + new errors → rollback (see Deployment Runbook)
• DB query timeout / pool saturation → kill long queries, scale connections
• External dependency failing → check status pages, add circuit breaker
• Memory/CPU spike → check Vercel function logs for infinite loops

Phase 3 — Mitigate (variable)

# Kill a runaway DB query
psql $DATABASE_URL -c "SELECT pg_terminate_backend(<pid>);"

# Scale DB connections (Supabase/Neon — adjust pool size)
# Vercel → Settings → Environment Variables → update DATABASE_POOL_MAX

# Enable maintenance mode (if you have a feature flag)
vercel env add MAINTENANCE_MODE true production
vercel --prod  # redeploy with flag

Phase 4 — Resolve & Postmortem

After incident is resolved, within 24 hours:

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

Postmortem template: docs/postmortems/YYYY-MM-DD-incident-title.md

Escalation Path

---

### 3. Database Maintenance Runbook

```markdown
# Database Maintenance Runbook — PostgreSQL
**Schedule:** Weekly vacuum (automated), monthly manual review  

## Backup

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

✅ Expected: File created, size > 0. pg_restore --list backup.dump | head -20 shows tables.

Verify backup is restorable (test monthly):

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

✅ Expected: Row count matches production.

Migration

# Always test in staging first
DATABASE_URL=$STAGING_DATABASE_URL npx prisma migrate deploy
# Verify, then:
DATABASE_URL=$PROD_DATABASE_URL npx prisma migrate deploy

✅ Expected: All migrations have been successfully applied.

⚠️ For large table migrations (> 1M rows), use pg_repack or add column with DEFAULT separately to avoid table locks.

Vacuum & Reindex

# Check bloat before deciding
psql $DATABASE_URL -c "
SELECT schemaname, tablename, 
       pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS total_size,
       n_dead_tup, n_live_tup,
       ROUND(n_dead_tup::numeric / NULLIF(n_live_tup + n_dead_tup, 0) * 100, 1) AS dead_ratio
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC LIMIT 10;"

# Vacuum high-bloat tables (non-blocking)
psql $DATABASE_URL -c "VACUUM ANALYZE users;"
psql $DATABASE_URL -c "VACUUM ANALYZE events;"

# Reindex (use CONCURRENTLY to avoid locks)
psql $DATABASE_URL -c "REINDEX INDEX CONCURRENTLY users_email_idx;"

✅ Expected: dead_ratio drops below 5% after vacuum.

---

## Staleness Detection

Add a staleness header to every runbook:

```markdown
## Staleness Check
This runbook references the following config files. If they've changed since the
"Last verified" date, review the affected steps.

| Config File | Last Modified | Affects Steps |
|-------------|--------------|---------------|
| vercel.json | `git log -1 --format=%ci -- vercel.json` | Step 3, Rollback |
| prisma/schema.prisma | `git log -1 --format=%ci -- prisma/schema.prisma` | Step 2, DB Maintenance |
| .github/workflows/deploy.yml | `git log -1 --format=%ci -- .github/workflows/deploy.yml` | Step 1, Step 3 |
| docker-compose.yml | `git log -1 --format=%ci -- docker-compose.yml` | All scaling steps |

Automation: Add a CI job that runs weekly and comments on the runbook doc if any referenced file was modified more recently than the runbook's "Last verified" date.

Runbook Testing Methodology

Dry-Run in Staging

Before trusting a runbook in production, validate every step in staging:

# 1. Create a staging environment mirror
vercel env pull .env.staging
source .env.staging

# 2. Run each step with staging credentials
# Replace all $DATABASE_URL with $STAGING_DATABASE_URL
# Replace all production URLs with staging URLs

# 3. Verify expected outputs match
# Document any discrepancies and update the runbook

# 4. Time each step — update estimates in the runbook
time npx prisma migrate deploy

Quarterly Review Cadence

Schedule a 1-hour review every quarter:

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

Common Pitfalls

Best Practices

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