Plan and execute a launch runbook covering pre-launch verification, go-live procedures, DNS cutover, post-launch monitoring, and rollback procedures. Use this skill whenever the user is preparing to launch a website or product, planning a DNS cutover, building a go-live checklist, or executing a launch day. Triggers on launch runbook, go-live, launch day, DNS cutover, deploy to production, site launch, product launch, cutover plan, launch checklist, deployment procedure. Also triggers when a launch is approaching and the team needs structured coordination, even if 'runbook' is not explicitly stated.
npx skills add https://github.com/rampstackco/claude-skills --skill launch-runbook
Plan and execute the launch of a website, product, or major release. The runbook is the document everyone uses on launch day. Stack-agnostic.
This skill is for the launch event. For pre-launch QA, use qa-testing. For post-launch incident handling, use incident-response.
qa-testing)incident-response)after-action-report)A launch has four phases. The runbook covers all four.
Verify everything is ready before the launch window.
T-30 days:
T-7 days:
T-1 day:
T-1 hour:
The actual launch. Sequenced steps with owners and verifications.
Standard cutover steps:
10. Run full smoke tests on production
11. Announce launch to internal team
12. Begin monitoring window
Each step has:
Confirm the launch is healthy.
Within first hour:
Within first 24 hours:
Monitor the long tail.
A launch has clear role assignments. Ambiguity here is the most common cause of launch chaos.
| Role | Responsibility |
|---|---|
| Launch lead | Owns the runbook. Calls go/no-go. Calls rollback. |
| Deploy operator | Executes the technical deploy steps. |
| QA lead | Runs verification tests and confirms each milestone. |
| Comms lead | Posts internal updates, manages external messaging. |
| On-call engineer | Available for issues during and after launch. |
| Stakeholder rep | Approves on behalf of business stakeholders. |
For small teams, one person may fill multiple roles. Each role's responsibilities should still be explicit.
Define before the launch. Decisions are easier to make pre-emptively than under pressure.
Automatic rollback triggers:
Discretionary rollback triggers:
Decision authority: The launch lead calls rollback. Pre-define who acts as deputy if launch lead is unavailable.
Default output: a markdown runbook at launch-runbook-[project].md plus supporting checklists.
Structure:
10. Contacts (escalation paths, on-call)
references/runbook-template.md - Fillable runbook template with example cutover sequences.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 rampstackco/launch-runbook 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.