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Skill

/deploy-checklist

Execute a structured deployment to staging or production. Use when asked to deploy, ship, release, push to production, or promote to staging.

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atlas-agents
58713 skills
Install
$ npx -y skills add agulli/atlas-agents --skill deploy-checklist --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/deploy-checklist

Context preview

The summary Claude sees to decide when to auto-load this skill.

Execute a structured deployment to staging or production. Use when asked to deploy, ship, release, push to production, or promote to staging.

SKILL.md

deploy-checklist.SKILL.md
name: deploy-checklist
description: Execute a structured deployment to staging or production. Use when asked to deploy, ship, release, push to production, or promote to staging.
license: MIT
compatibility: Requires git and access to the deployment target
allowed-tools: Bash(git:*) Bash(docker:*) Bash(kubectl:*)

Clarification Gates

You must have unambiguous answers to these before touching anything:

  • **Target:** staging or production? (Never assume. Never infer from branch name.)
  • **Deploying what:** which service, which version/tag/commit SHA?
  • **Rollback plan:** does one exist, and do you have access to execute it?

If the user says "deploy the thing" without specifying the environment, that is not sufficient information. Ask.

> **Model Proposes, Code Disposes:** You propose the deploy sequence. The CI/CD system or platform CLI executes it. You do not write deployment commands from memory — you use exactly the command documented in the project's deploy runbook or Makefile.

Pre-deployment

1. **Confirm the target environment.** Ask if not specified: staging or production? Never assume production. 2. **Check the branch.** Verify you are on the correct branch:

   git branch --show-current

3. **Check for uncommitted changes:**

   git status --porcelain

If there are uncommitted changes, STOP. Commit or stash them first. 4. **Run the test suite.** Execute the project's full test command. If any test fails, STOP. Do not deploy with failing tests. 5. **Check the changelog/diff.** Run `git log --oneline main..HEAD` to see what will be deployed. Present this to the user.

Deployment

6. **Execute the deploy command.** Use the project's existing deployment mechanism:

  • Docker: `docker build && docker push`
  • Kubernetes: `kubectl apply -f`
  • Platform-specific: `fly deploy`, `vercel --prod`, `gcloud run deploy`

Use exactly the command documented in the project. Do not invent deployment commands.

7. **Wait for deployment to complete.** Do not proceed until the deployment tool confirms success.

Post-deployment

8. **Smoke test.** Hit the health endpoint or run a basic functional test:

   curl -s https://<deployed-url>/health | jq .

9. **Monitor for 2 minutes.** Check error rates and latency. If either spikes, recommend an immediate rollback.

Rationalizations

| Excuse | Rebuttal | |--------|----------| | "Tests are slow, I'll skip them" | Deploying untested code is how you get paged at 3 AM. Run the tests. | | "It works on my machine" | That is not evidence. The test suite is evidence. | | "I'll just deploy and fix issues as they come" | You have no rollback plan if you don't know what changed. Check the diff first. |

Verification

  • [ ] Target environment was explicitly confirmed (not assumed)
  • [ ] All tests passed before deployment
  • [ ] Deployment tool reported success
  • [ ] Health check returned 200 after deployment
  • [ ] No error spike observed in the first 2 minutes
Read more
Ships withatlas-agents

This repository contains the source code, examples, and project implementations for the book "Hands-on AI Agents" — a practical, code-first guide to building AI agents and, more importantly, putting them into production.

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