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Ci Cd Pipeline

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vibeeval
ci-cd-pipeline

GitHub Actions workflow patterns, matrix builds, caching strategies, deployment pipelines, artifact management, and rollback procedures.

Overview

Publishervibeeval
Repositoryvibecosystem
Skill nameci-cd-pipeline
Stars
533
Forks
44
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 vibeeval on GitHub. Read the source before you install it.

Installation

Install the Ci Cd Pipeline 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.
    https://github.com/vibeeval/vibecosystem/tree/main/skills/ci-cd-pipeline
  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/vibeeval/vibecosystem.git /tmp/vibecosystem
mkdir -p .claude/skills
cp -r /tmp/vibecosystem/skills/ci-cd-pipeline .claude/skills/ci-cd-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Cd Pipeline 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 Pipeline 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 Pipeline 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 Pipeline Patterns

GitHub Actions workflows for consistent, fast, and reliable delivery pipelines.

Workflow Structure

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

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]
  workflow_dispatch:        # manual trigger

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true  # cancel outdated runs on same branch

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'
      - run: npm ci
      - run: npm run lint

  test:
    runs-on: ubuntu-latest
    needs: lint              # runs after lint passes
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'
      - run: npm ci
      - run: npm test -- --coverage

  build:
    runs-on: ubuntu-latest
    needs: test
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'
      - run: npm ci
      - run: npm run build

Matrix Builds

Test across multiple versions and operating systems in parallel.

yaml
jobs:
  test:
    strategy:
      fail-fast: false       # don't cancel others if one fails
      matrix:
        node: ['18', '20', '22']
        os: [ubuntu-latest, macos-latest, windows-latest]
        exclude:
          - os: windows-latest
            node: '18'       # skip this combination

    runs-on: ${{ matrix.os }}
    name: Test Node ${{ matrix.node }} on ${{ matrix.os }}

    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: ${{ matrix.node }}
          cache: 'npm'
      - run: npm ci
      - run: npm test
yaml
# Python matrix example
jobs:
  test:
    strategy:
      matrix:
        python: ['3.10', '3.11', '3.12']
        django: ['4.2', '5.0']
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python }}
      - run: pip install django==${{ matrix.django }} -r requirements-test.txt
      - run: pytest

Dependency Caching

yaml
# Node.js: cache npm or yarn
- uses: actions/setup-node@v4
  with:
    node-version: '20'
    cache: 'npm'             # auto-caches ~/.npm

# Manual cache (more control)
- uses: actions/cache@v4
  id: npm-cache
  with:
    path: ~/.npm
    key: npm-${{ runner.os }}-${{ hashFiles('**/package-lock.json') }}
    restore-keys: |
      npm-${{ runner.os }}-

- run: npm ci
yaml
# Python: cache pip
- uses: actions/cache@v4
  with:
    path: ~/.cache/pip
    key: pip-${{ runner.os }}-${{ hashFiles('**/requirements*.txt') }}
    restore-keys: |
      pip-${{ runner.os }}-

- run: pip install -r requirements.txt
yaml
# Docker layer caching
- uses: docker/setup-buildx-action@v3

- uses: actions/cache@v4
  with:
    path: /tmp/.buildx-cache
    key: buildx-${{ runner.os }}-${{ github.sha }}
    restore-keys: |
      buildx-${{ runner.os }}-

- uses: docker/build-push-action@v5
  with:
    cache-from: type=local,src=/tmp/.buildx-cache
    cache-to: type=local,dest=/tmp/.buildx-cache-new,mode=max

# Prevent cache from growing unbounded
- run: |
    rm -rf /tmp/.buildx-cache
    mv /tmp/.buildx-cache-new /tmp/.buildx-cache

Artifact Upload/Download Between Jobs

yaml
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npm ci && npm run build

      - uses: actions/upload-artifact@v4
        with:
          name: build-output
          path: dist/
          retention-days: 7

  deploy:
    runs-on: ubuntu-latest
    needs: build
    steps:
      - uses: actions/download-artifact@v4
        with:
          name: build-output
          path: dist/

      - run: ls -la dist/    # verify artifact present
      - run: ./scripts/deploy.sh

Environment Secrets Management

yaml
# Repository secrets: Settings → Secrets and variables → Actions
# Environment secrets: require approval before use

jobs:
  deploy-production:
    environment: production     # requires approval + uses env secrets
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Deploy
        env:
          API_KEY: ${{ secrets.API_KEY }}
          DATABASE_URL: ${{ secrets.DATABASE_URL }}
        run: ./scripts/deploy.sh

      # Never echo secrets
      # ✅ GitHub masks them automatically in logs
      # ❌ Don't: run: echo ${{ secrets.API_KEY }}

Full Deployment Pipeline

yaml
# .github/workflows/deploy.yml
name: Deploy

on:
  push:
    branches: [main]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20', cache: 'npm' }
      - run: npm ci && npm run lint

  test:
    runs-on: ubuntu-latest
    needs: lint
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20', cache: 'npm' }
      - run: npm ci && npm test

  build-and-push:
    runs-on: ubuntu-latest
    needs: test
    outputs:
      image-tag: ${{ steps.meta.outputs.tags }}

    steps:
      - uses: actions/checkout@v4

      - uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - uses: docker/metadata-action@v5
        id: meta
        with:
          images: ghcr.io/${{ github.repository }}
          tags: |
            type=sha,prefix=sha-
            type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}

      - uses: docker/build-push-action@v5
        with:
          push: true
          tags: ${{ steps.meta.outputs.tags }}

  deploy-staging:
    runs-on: ubuntu-latest
    needs: build-and-push
    environment: staging
    steps:
      - run: |
          ssh deploy@staging.example.com \
            "docker pull ghcr.io/${{ github.repository }}:latest && \
             docker compose up -d"

  smoke-test:
    runs-on: ubuntu-latest
    needs: deploy-staging
    steps:
      - run: |
          sleep 10
          curl -f https://staging.example.com/health || exit 1

  deploy-production:
    runs-on: ubuntu-latest
    needs: smoke-test
    environment: production       # requires manual approval
    steps:
      - run: |
          ssh deploy@prod.example.com \
            "docker pull ghcr.io/${{ github.repository }}:latest && \
             docker compose up -d"

Conditional Job Execution

yaml
jobs:
  deploy:
    if: github.ref == 'refs/heads/main' && github.event_name == 'push'
    runs-on: ubuntu-latest
    steps:
      - run: echo "Deploying main branch"

  notify-failure:
    if: failure()               # only runs if previous job failed
    needs: [lint, test, build]
    runs-on: ubuntu-latest
    steps:
      - uses: actions/github-script@v7
        with:
          script: |
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: 'CI failed. Please check the logs.'
            })

Reusable Workflows and Composite Actions

yaml
# .github/workflows/reusable-test.yml
name: Reusable Test Workflow

on:
  workflow_call:
    inputs:
      node-version:
        required: false
        type: string
        default: '20'
    secrets:
      NPM_TOKEN:
        required: false

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: ${{ inputs.node-version }}
      - run: npm ci && npm test
yaml
# Caller workflow
jobs:
  test:
    uses: ./.github/workflows/reusable-test.yml
    with:
      node-version: '22'
    secrets:
      NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
yaml
# Composite action: .github/actions/setup-app/action.yml
name: Setup Application
description: Install deps and configure environment

runs:
  using: composite
  steps:
    - uses: actions/setup-node@v4
      with:
        node-version: '20'
        cache: 'npm'

    - run: npm ci
      shell: bash

    - run: npm run db:migrate
      shell: bash
      env:
        DATABASE_URL: ${{ env.DATABASE_URL }}

Deployment Strategies

yaml
# Blue-Green: switch traffic between two identical environments
- name: Deploy to Blue
  run: |
    kubectl set image deployment/app-blue app=my-image:${{ github.sha }}
    kubectl rollout status deployment/app-blue

- name: Run smoke tests on Blue
  run: curl -f https://blue.example.com/health

- name: Switch traffic to Blue
  run: |
    kubectl patch service app \
      -p '{"spec":{"selector":{"slot":"blue"}}}'

# Canary: gradual traffic shift
- name: Deploy Canary (10% traffic)
  run: |
    kubectl set image deployment/app-canary app=my-image:${{ github.sha }}
    kubectl patch virtualservice app \
      --type merge \
      -p '{"spec":{"http":[{"route":[
        {"destination":{"host":"app-stable"},"weight":90},
        {"destination":{"host":"app-canary"},"weight":10}
      ]}]}}'

Rollback Procedures

yaml
- name: Deploy with rollback on failure
  id: deploy
  run: |
    PREVIOUS_IMAGE=$(kubectl get deployment app -o jsonpath='{.spec.template.spec.containers[0].image}')
    echo "previous-image=$PREVIOUS_IMAGE" >> $GITHUB_OUTPUT

    kubectl set image deployment/app app=my-image:${{ github.sha }}
    kubectl rollout status deployment/app --timeout=5m || {
      echo "Rollback triggered"
      kubectl set image deployment/app app=$PREVIOUS_IMAGE
      kubectl rollout status deployment/app
      exit 1
    }
bash
# Manual rollback command (kubectl)
kubectl rollout undo deployment/app
kubectl rollout undo deployment/app --to-revision=3

# Docker Compose rollback
docker compose pull && docker compose up -d
# or pin to previous image tag
IMAGE_TAG=sha-abc123 docker compose up -d

Scheduled Jobs and PR Automation

yaml
# Scheduled dependency update check
on:
  schedule:
    - cron: '0 9 * * 1'      # every Monday at 09:00 UTC

jobs:
  check-deps:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npm outdated || true
      - run: npx npm-check-updates --errorLevel 2
yaml
# Auto-label PR based on files changed
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  label:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/labeler@v5
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          configuration-path: .github/labeler.yml

Status Badges

markdown
<!-- README.md badges -->
![CI](https://github.com/owner/repo/actions/workflows/ci.yml/badge.svg)
![Coverage](https://codecov.io/gh/owner/repo/branch/main/graph/badge.svg)

Key principle: Fail fast in early stages (lint before test), cache aggressively, require approval gates for production, always have a rollback path.

Frequently asked questions

What does the Ci Cd Pipeline AI skill do?

GitHub Actions workflow patterns, matrix builds, caching strategies, deployment pipelines, artifact management, and rollback procedures.

Why use Ci Cd Pipeline on TypingMind?

Because you install it once and use it with any model. Ci Cd Pipeline 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 Pipeline in TypingMind?

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

Which AI models can use Ci Cd Pipeline?

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 Pipeline?

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

Is the Ci Cd Pipeline AI skill free?

Yes. It is published on GitHub by vibeeval 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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