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Runbook

Skill tinh2/skills-hub-registry/docs/runbook

Open registry of community-contributed AI coding skills (SKILL.md files) — daily-synced to skills-hub.ai. Install across Claude Code, Cursor, Codex CLI, Windsurf, Copilot, and any MCP-compatible tool with one command.

Install
npx -y skills add tinh2/skills-hub-registry --skill runbook

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Generate an operations runbook from your deployment config, Docker/K8s manifests, CI/CD pipelines, and monitoring setup. Produces copy-pasteable procedures for deployment, rollback, scaling, database maintenance, incident response, and troubleshooting. Use when you need a runbook, ops playbook, deployment guide, incident response plan, or production operations documentation.

SKILL.md

7.6 KB, as published. Nobody here has run it

You are in AUTONOMOUS MODE. Do NOT ask questions. Analyze the project's infrastructure and operations artifacts to produce a comprehensive runbook.

INPUT: $ARGUMENTS

Accepted arguments:

  • No arguments: generate a complete operations runbook.
  • deployment: focus on deployment procedures only.
  • incident: focus on incident response procedures only.
  • database: focus on database maintenance procedures only.
  • troubleshooting: focus on troubleshooting guides only.

============================================================ PHASE 1: INFRASTRUCTURE DISCOVERY

Step 1.1 -- Deployment Configuration

Scan for deployment artifacts:

TypeFiles to Check
DockerDockerfile, docker-compose.yml, docker-compose.*.yml
Kubernetesk8s/, kubernetes/, manifests/, *.yaml with apiVersion:
HelmChart.yaml, values.yaml, templates/
Terraform*.tf, terraform/, infrastructure/
Serverlessserverless.yml, SAM template.yaml
PaaSProcfile, fly.toml, vercel.json, netlify.toml, app.yaml
PM2ecosystem.config.js

Step 1.2 -- CI/CD Pipeline

Scan for: .github/workflows/*.yml, .gitlab-ci.yml, Jenkinsfile, .circleci/config.yml, bitbucket-pipelines.yml, cloudbuild.yaml, azure-pipelines.yml.

Read each pipeline file and extract build steps, deployment targets, environment variables, secrets referenced, and branch triggers.

Step 1.3 -- Database Configuration

Scan for: migration files (migrations/, prisma/migrations/, alembic/, db/migrate/), schema files (schema.prisma, models.py, *.entity.ts), seed files, database config (database.yml, knexfile.js), and backup scripts.

Step 1.4 -- Monitoring and Observability

Scan for: logging config (winston, pino, log4j), APM (newrelic, datadog, elastic-apm), metrics (prometheus, grafana), error tracking (sentry, bugsnag, rollbar), health check endpoints, and alerting rules.

Step 1.5 -- Environment Configuration

Scan for: .env.example, environment-specific configs, feature flags, and secrets management (vault, AWS SSM, sealed secrets).

============================================================ PHASE 2: RUNBOOK GENERATION

Generate docs/runbook.md with these sections. Only include sections relevant to detected infrastructure. Every procedure must have copy-pasteable commands.

The runbook MUST cover these areas (skip if not applicable to project):

  1. Deployment -- Prerequisites, step-by-step deploy commands per environment, post-deploy verification checklist (health check, logs, smoke test, metrics).

  2. Rollback -- Quick rollback procedure (< 5 min, redeploy previous version), full rollback with database revert if needed, verification checklist.

  3. Scaling -- Scale up/down commands, auto-scaling config and thresholds.

  4. Database Maintenance -- Run/rollback migrations, create/restore backups, common diagnostic queries (table sizes, active connections, slow queries).

  5. Incident Response -- Severity levels (SEV1-4 with response times and examples), incident checklist (acknowledge, communicate, investigate, mitigate, resolve, postmortem), first-response diagnostic commands for logs, status, and resources.

  6. Monitoring -- Dashboard links, key metrics with normal ranges, alert response guide (high CPU, memory, error rate, disk, health check failures with actions).

  7. Troubleshooting -- Common failure scenarios (app won't start, connection timeouts, performance degradation) with step-by-step diagnosis. Error-to-solution lookup table.

Format: use markdown headers, numbered steps with fenced bash blocks for commands, checklists for verification, and tables for reference data.

If a runbook already exists, read it first and preserve manually-written sections.

============================================================ PHASE 3: VALIDATE AND WRITE

For each command in the runbook:

  • Verify the referenced tool/CLI exists in the project's dependencies or Dockerfile
  • Verify paths and file references are correct
  • Verify environment variable names match what the project uses

Write the runbook to docs/runbook.md.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing documentation, validate completeness:

  1. Verify all required sections are present and non-empty.
  2. Verify internal cross-references and links resolve correctly.
  3. Verify no placeholder text remains ("{TODO}", "[TBD]", "...", "etc.").
  4. Verify code examples are syntactically valid.

IF VALIDATION FAILS:

  • Identify which sections are incomplete or contain placeholders
  • Re-generate only the deficient sections
  • Repeat up to 2 iterations

============================================================ OUTPUT

Runbook Generated

Infrastructure Detected

  • Deployment: [Docker / K8s / Serverless / etc.]
  • CI/CD: [GitHub Actions / GitLab CI / etc.]
  • Database: [PostgreSQL / MongoDB / etc.]
  • Monitoring: [Datadog / Prometheus / etc.]
  • Environments: [dev, staging, production]

Sections Generated

  • Deployment procedures
  • Rollback procedures
  • Scaling instructions
  • Database maintenance
  • Incident response
  • Monitoring and alerts
  • Troubleshooting guide

File Written

  • docs/runbook.md

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

  • Look for the project path in ~/.claude/projects/
  • If found, append to skill-telemetry.md in that memory directory

Entry format:

### /runbook — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.

============================================================ DO NOT

  • Do NOT fabricate infrastructure details. Only document what is evident from config files.
  • Do NOT include actual secrets, passwords, or API keys in the runbook.
  • Do NOT include placeholder commands for infrastructure that does not exist.
  • Do NOT generate a runbook for projects with no deployment configuration (e.g., a library or CLI tool). Report that no runbook is needed and suggest /readme.
  • Do NOT include generic advice not specific to this project's stack.

NEXT STEPS:

After generating the runbook:

  • "Run /document to check overall documentation health."
  • "Run /diagram to generate infrastructure diagrams."
  • "Run /devops to review and improve the deployment pipeline."

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.