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deploy

Skill3 starsChanged 6 months agoMITView on GitHub

Skill to automate the full deployment process including build, test, and release steps

  • Commits and pushes

155 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use pm-discovery, qa-phase and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ww-w-aiSkill

    pm-discovery

    PM Agent Team - Automated product discovery, strategy, and PRD generation. Runs PM analysis workflow to produce comprehensive PRD before PDCA Plan phase.

    Also in 3 projects that use this

  • ww-w-aiSkill

    qa-phase

    Run QA tests at various levels (L1-L5) for features under PDCA management. Integrates with MCP tools to execute structured test plans.

    Also in 3 projects that use this

  • +10 more from ww-w-ai

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 138 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If tests fail → STOP. Report failures.

    line 28
  2. Distinctive:

    No error logs in initial 30 seconds

    line 67
  3. Distinctive:

    All smoke tests pass

    line 68

“Common” is measured: kept by at least 6 in 10 of the 138 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Deploy Skill

Purpose

Automate the deployment pipeline: build → test → deploy → verify.

Prerequisites

  • All tests passing
  • Environment variables configured
  • Access to deployment target

Steps

1. Verify Clean State

git status
git pull origin main

2. Run Tests

npm test

If tests fail → STOP. Report failures.

3. Build

npm run build

4. Deploy by Environment

Development:

docker-compose -f docker-compose.dev.yml up -d --build

Production:

docker-compose -f docker-compose.prod.yml pull
docker-compose -f docker-compose.prod.yml up -d

5. Health Check

curl -f http://localhost:3000/health || echo "Health check failed"

6. Tail Logs (first 30 seconds)

docker-compose logs --tail=50 -f app

Rollback

git revert HEAD --no-edit
git push origin main
# Redeploy previous version

Success Criteria

  • Health endpoint returns 200
  • No error logs in initial 30 seconds
  • All smoke tests pass

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

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Now do this for your own.

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