> Generate operational runbooks from codebase analysis covering deployment, incident response, scaling, and monitoring, with copy-paste commands and rollback steps. Use when bootstrapping ops docs, preparing for on-call, or post-incident.
npx skills add https://github.com/borghei/Claude-Skills --skill runbook-generator
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
Before generating the runbook, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|------|---------|---------|
| runbook_scaffolder.py | Generate runbook markdown templates from a JSON service definition | python scripts/runbook_scaffolder.py -i service.json --type deployment -o runbook.md |
| runbook_validator.py | Validate runbook markdown for completeness and quality (required sections, VERIFY blocks, hardcoded creds, escalation table) | python scripts/runbook_validator.py --dir docs/runbooks --strict |
| staleness_checker.py | Check runbook freshness against configurable staleness thresholds | python scripts/staleness_checker.py docs/runbooks --threshold 90 --json |
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
This skill does NOT cover:
ci-cd-pipeline-builder for automated pipelines)migration-architect for schema migration tooling)observability-designer for monitoring infrastructure)skill-security-auditor for security-focused analysis)| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| ci-cd-pipeline-builder | Runbook deployment steps align with pipeline stages | Pipeline config feeds into deployment runbook generation; runbook rollback steps reference pipeline rollback triggers |
| observability-designer | Monitoring runbook references alert rules and dashboards | Observability outputs (alert names, dashboard URLs) are embedded in runbook VERIFY and Monitor steps |
| migration-architect | Database maintenance runbook uses migration tooling conventions | Migration file paths and commands flow into the database runbook template; rollback steps mirror migration rollback commands |
| release-manager | Release process triggers runbook execution checkpoints | Release tags and changelogs feed into runbook staleness checks; release gates reference runbook pre-deployment checklists |
| env-secrets-manager | Runbook commands reference env vars managed by secrets tooling | Secret names and vault paths flow into runbook env var references; rotation schedules inform runbook update cadence |
| changelog-generator | Post-deployment runbook steps cross-reference changelog entries | Changelog diffs help identify which runbook steps need re-verification after a release |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take borghei/runbook-generator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.