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Ci Cd And Automation

CommunityPopular
addyosmani
ci-cd-and-automation

Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.

Overview

Publisheraddyosmani
Repositoryagent-skills
Skill nameci-cd-and-automation
Stars
95.8K
Forks
10.1K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by addyosmani on GitHub. Read the source before you install it.

Installation

Install the Ci Cd And Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/addyosmani/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/ci-cd-and-automation .claude/skills/ci-cd-and-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Cd And Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ci Cd And Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ci Cd And Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CI/CD and Automation

Overview

Automate quality gates so that no change reaches production without passing tests, lint, type checking, and build. CI/CD is the enforcement mechanism for every other skill — it catches what humans and agents miss, and it does so consistently on every single change.

Shift Left: Catch problems as early in the pipeline as possible. A bug caught in linting costs minutes; the same bug caught in production costs hours. Move checks upstream — static analysis before tests, tests before staging, staging before production.

Faster is Safer: Smaller batches and more frequent releases reduce risk, not increase it. A deployment with 3 changes is easier to debug than one with 30. Frequent releases build confidence in the release process itself.

When to Use

  • Setting up a new project's CI pipeline
  • Adding or modifying automated checks
  • Configuring deployment pipelines
  • When a change should trigger automated verification
  • Debugging CI failures

The Quality Gate Pipeline

Every change goes through these gates before merge:

Pull Request Opened
┌─────────────────┐
│   LINT CHECK     │  eslint, prettier
│   ↓ pass         │
│   TYPE CHECK     │  tsc --noEmit
│   ↓ pass         │
│   UNIT TESTS     │  jest/vitest
│   ↓ pass         │
│   BUILD          │  npm run build
│   ↓ pass         │
│   INTEGRATION    │  API/DB tests
│   ↓ pass         │
│   E2E (optional) │  Playwright/Cypress
│   ↓ pass         │
│   SECURITY AUDIT │  npm audit
│   ↓ pass         │
│   BUNDLE SIZE    │  bundlesize check
└─────────────────┘
  Ready for review

No gate can be skipped. If lint fails, fix lint — don't disable the rule. If a test fails, fix the code — don't skip the test.

GitHub Actions Configuration

Basic CI Pipeline

yaml
# .github/workflows/ci.yml
name: CI

on:
  pull_request:
    branches: [main]
  push:
    branches: [main]

jobs:
  quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Lint
        run: npm run lint

      - name: Type check
        run: npx tsc --noEmit

      - name: Test
        run: npm test -- --coverage

      - name: Build
        run: npm run build

      - name: Security audit
        run: npm audit --audit-level=high

With Database Integration Tests

yaml
  integration:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_DB: testdb
          POSTGRES_USER: ci_user
          POSTGRES_PASSWORD: ${{ secrets.CI_DB_PASSWORD }}
        ports:
          - 5432:5432
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'
      - run: npm ci
      - name: Run migrations
        run: npx prisma migrate deploy
        env:
          DATABASE_URL: postgresql://ci_user:${{ secrets.CI_DB_PASSWORD }}@localhost:5432/testdb
      - name: Integration tests
        run: npm run test:integration
        env:
          DATABASE_URL: postgresql://ci_user:${{ secrets.CI_DB_PASSWORD }}@localhost:5432/testdb

Note: Even for CI-only test databases, use GitHub Secrets for credentials rather than hardcoding values. This builds good habits and prevents accidental reuse of test credentials in other contexts.

E2E Tests

yaml
  e2e:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'
      - run: npm ci
      - name: Install Playwright
        run: npx playwright install --with-deps chromium
      - name: Build
        run: npm run build
      - name: Run E2E tests
        run: npx playwright test
      - uses: actions/upload-artifact@v4
        if: failure()
        with:
          name: playwright-report
          path: playwright-report/

Feeding CI Failures Back to Agents

The power of CI with AI agents is the feedback loop. When CI fails:

CI fails
Copy the failure output
Feed it to the agent:
"The CI pipeline failed with this error:
[paste specific error]
Fix the issue and verify locally before pushing again."
Agent fixes → pushes → CI runs again

Key patterns:

Lint failure → Agent runs `npm run lint --fix` and commits
Type error  → Agent reads the error location and fixes the type
Test failure → Agent follows debugging-and-error-recovery skill
Build error → Agent checks config and dependencies

Deployment Strategies

Preview Deployments

Every PR gets a preview deployment for manual testing:

yaml
# Deploy preview on PR (Vercel/Netlify/etc.)
deploy-preview:
  runs-on: ubuntu-latest
  if: github.event_name == 'pull_request'
  steps:
    - uses: actions/checkout@v4
    - name: Deploy preview
      run: npx vercel --token=${{ secrets.VERCEL_TOKEN }}

Feature Flags

Feature flags decouple deployment from release. Deploy incomplete or risky features behind flags so you can:

  • Ship code without enabling it. Merge to main early, enable when ready.
  • Roll back without redeploying. Disable the flag instead of reverting code.
  • Canary new features. Enable for 1% of users, then 10%, then 100%.
  • Run A/B tests. Compare behavior with and without the feature.
typescript
// Simple feature flag pattern
if (featureFlags.isEnabled('new-checkout-flow', { userId })) {
  return renderNewCheckout();
}
return renderLegacyCheckout();

Flag lifecycle: Create → Enable for testing → Canary → Full rollout → Remove the flag and dead code. Flags that live forever become technical debt — set a cleanup date when you create them.

Staged Rollouts

PR merged to main
  Staging deployment (auto)
    │ Manual verification
  Production deployment (manual trigger or auto after staging)
  Monitor for errors (15-minute window)
    ├── Errors detected → Rollback
    └── Clean → Done

Rollback Plan

Every deployment should be reversible:

yaml
# Manual rollback workflow
name: Rollback
on:
  workflow_dispatch:
    inputs:
      version:
        description: 'Version to rollback to'
        required: true

jobs:
  rollback:
    runs-on: ubuntu-latest
    steps:
      - name: Rollback deployment
        run: |
          # Deploy the specified previous version
          npx vercel rollback ${{ inputs.version }}

Environment Management

.env.example       → Committed (template for developers)
.env                → NOT committed (local development)
.env.test           → Committed (test environment, no real secrets)
CI secrets          → Stored in GitHub Secrets / vault
Production secrets  → Stored in deployment platform / vault

CI should never have production secrets. Use separate secrets for CI testing.

Automation Beyond CI

Dependabot / Renovate

yaml
# .github/dependabot.yml
version: 2
updates:
  - package-ecosystem: npm
    directory: /
    schedule:
      interval: weekly
    open-pull-requests-limit: 5

Build Cop Role

Designate someone responsible for keeping CI green. When the build breaks, the Build Cop's job is to fix or revert — not the person whose change caused the break. This prevents broken builds from accumulating while everyone assumes someone else will fix it.

PR Checks

  • Required reviews: At least 1 approval before merge
  • Required status checks: CI must pass before merge
  • Branch protection: No force-pushes to main
  • Auto-merge: If all checks pass and approved, merge automatically

CI Optimization

When the pipeline exceeds 10 minutes, apply these strategies in order of impact:

Slow CI pipeline?
├── Cache dependencies
│   └── Use actions/cache or setup-node cache option for node_modules
├── Run jobs in parallel
│   └── Split lint, typecheck, test, build into separate parallel jobs
├── Only run what changed
│   └── Use path filters to skip unrelated jobs (e.g., skip e2e for docs-only PRs)
├── Use matrix builds
│   └── Shard test suites across multiple runners
├── Optimize the test suite
│   └── Remove slow tests from the critical path, run them on a schedule instead
└── Use larger runners
    └── GitHub-hosted larger runners or self-hosted for CPU-heavy builds

Example: caching and parallelism

yaml
jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '22', cache: 'npm' }
      - run: npm ci
      - run: npm run lint

  typecheck:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '22', cache: 'npm' }
      - run: npm ci
      - run: npx tsc --noEmit

  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '22', cache: 'npm' }
      - run: npm ci
      - run: npm test -- --coverage

Common Rationalizations

RationalizationReality
"CI is too slow"Optimize the pipeline (see CI Optimization below), don't skip it. A 5-minute pipeline prevents hours of debugging.
"This change is trivial, skip CI"Trivial changes break builds. CI is fast for trivial changes anyway.
"The test is flaky, just re-run"Flaky tests mask real bugs and waste everyone's time. Fix the flakiness.
"We'll add CI later"Projects without CI accumulate broken states. Set it up on day one.
"Manual testing is enough"Manual testing doesn't scale and isn't repeatable. Automate what you can.

Red Flags

  • No CI pipeline in the project
  • CI failures ignored or silenced
  • Tests disabled in CI to make the pipeline pass
  • Production deploys without staging verification
  • No rollback mechanism
  • Secrets stored in code or CI config files (not secrets manager)
  • Long CI times with no optimization effort

Verification

After setting up or modifying CI:

  • All quality gates are present (lint, types, tests, build, audit)
  • Pipeline runs on every PR and push to main
  • Failures block merge (branch protection configured)
  • CI results feed back into the development loop
  • Secrets are stored in the secrets manager, not in code
  • Deployment has a rollback mechanism
  • Pipeline runs in under 10 minutes for the test suite

Frequently asked questions

What does the Ci Cd And Automation AI skill do?

Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.

Why use Ci Cd And Automation on TypingMind?

Because you install it once and use it with any model. Ci Cd And Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ci Cd And Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/addyosmani/agent-skills/tree/main/skills/ci-cd-and-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ci Cd And Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ci Cd And Automation?

As many as you like. As long as a model supports skills, you can use Ci Cd And Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ci Cd And Automation AI skill free?

Yes. It is published on GitHub by addyosmani under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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