mcpbeat Sign in

Deploy Checklist Agent Skill

Pre-deploy and post-deploy checklist skill. Ensures env vars, migrations, CI, rollback plan, smoke tests, and monitoring are verified before and after every deployment.

704 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
164
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/wednesday-solutions/ai-agent-skills --skill deploy-checklist

The instruction itself

8 sections, as written by the author

Deploy Checklist Skill

Trigger

Load this skill when a dev is about to deploy or has just deployed:

  • "We're deploying to production"
  • "Pre-deploy check"
  • "Post-deploy verification"
  • "Run the deploy checklist"
  • "Is it safe to deploy?"

Do NOT use this skill for: committing code (use git-os), creating a PR (use pr-create), or planning a project (use greenfield). This skill only applies at the deployment stage — code is already merged.


Run this checklist before and after every production deployment.

Pre-Deploy Checklist

  • [ ] All CI checks green on the deploy branch
  • [ ] Environment variables verified in target environment (no missing keys)
  • [ ] Database migrations reviewed — irreversible migrations documented
  • [ ] Migrations have been dry-run or tested in staging
  • [ ] Rollback plan documented: what to revert and how
  • [ ] Feature flags set correctly for the release
  • [ ] Downstream services notified if API contracts changed
  • [ ] Changelog updated with this release's changes
  • [ ] Deployment window confirmed (avoid peak traffic)

Deploy

  • [ ] Deploy initiated with correct branch / tag
  • [ ] Deployment logs monitored in real time
  • [ ] No unexpected errors during startup

Post-Deploy Checklist

  • [ ] Smoke test: critical user flows verified manually or via synthetic monitoring
  • [ ] Health check endpoint returns 200
  • [ ] Error rate in monitoring (Datadog, Grafana, Sentry) is normal
  • [ ] No spike in latency or DB query time
  • [ ] Monitoring alerts reviewed — no new alerts triggered
  • [ ] Changelog published / communicated to stakeholders
  • [ ] Ticket status updated (closed / released)

Rollback Trigger Criteria

Initiate rollback immediately if:

  • Error rate rises above 1% of requests
  • P95 latency increases by more than 2x baseline
  • Any data integrity issue detected
  • Critical feature path returns 5xx

Tools

| Action | Tool |

|--------|------|

| Run lint, test, build scripts | Bash |

| Check health endpoint | Bash — curl -s <url>/health |

| Read config or env files | Read |

| Check CI status | Bash — gh run list or gh pr checks |

Notes

  • Never deploy on Fridays unless it's a critical hotfix
  • Always have a second engineer available during production deploys
  • Document the actual deploy time and outcome in the ticket

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take wednesday-solutions/deploy-checklist from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.