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Github Actions Templates

CommunityPopular
wshobson
github-actions-templates

Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.

Overview

Publisherwshobson
Repositoryagents
Skill namegithub-actions-templates
Stars
39.8K
Forks
4.2K
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 wshobson on GitHub. Read the source before you install it.

Installation

Install the Github Actions Templates 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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/cicd-automation/skills/github-actions-templates .claude/skills/github-actions-templates
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Actions Templates 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 Github Actions Templates 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 Github Actions Templates 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.

GitHub Actions Templates

Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications.

Purpose

Create efficient, secure GitHub Actions workflows for continuous integration and deployment across various tech stacks.

When to Use

  • Automate testing and deployment
  • Build Docker images and push to registries
  • Deploy to Kubernetes clusters
  • Run security scans
  • Implement matrix builds for multiple environments

Common Workflow Patterns

Pattern 1: Test Workflow

yaml
name: Test

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

jobs:
  test:
    runs-on: ubuntu-latest

    strategy:
      matrix:
        node-version: [18.x, 20.x]

    steps:
      - uses: actions/checkout@v4

      - name: Use Node.js ${{ matrix.node-version }}
        uses: actions/setup-node@v4
        with:
          node-version: ${{ matrix.node-version }}
          cache: "npm"

      - name: Install dependencies
        run: npm ci

      - name: Run linter
        run: npm run lint

      - name: Run tests
        run: npm test

      - name: Upload coverage
        uses: codecov/codecov-action@v4
        with:
          files: ./coverage/lcov.info

Reference: See assets/test-workflow.yml

Pattern 2: Build and Push Docker Image

yaml
name: Build and Push

on:
  push:
    branches: [main]
    tags: ["v*"]

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  build:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write

    steps:
      - uses: actions/checkout@v4

      - name: Log in to Container Registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Extract metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=ref,event=branch
            type=ref,event=pr
            type=semver,pattern={{version}}
            type=semver,pattern={{major}}.{{minor}}

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          cache-from: type=gha
          cache-to: type=gha,mode=max

Reference: See assets/deploy-workflow.yml

Pattern 3: Deploy to Kubernetes

yaml
name: Deploy to Kubernetes

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-west-2

      - name: Update kubeconfig
        run: |
          aws eks update-kubeconfig --name production-cluster --region us-west-2

      - name: Deploy to Kubernetes
        run: |
          kubectl apply -f k8s/
          kubectl rollout status deployment/my-app -n production
          kubectl get services -n production

      - name: Verify deployment
        run: |
          kubectl get pods -n production
          kubectl describe deployment my-app -n production

Pattern 4: Matrix Build

yaml
name: Matrix Build

on: [push, pull_request]

jobs:
  build:
    runs-on: ${{ matrix.os }}

    strategy:
      matrix:
        os: [ubuntu-latest, macos-latest, windows-latest]
        python-version: ["3.9", "3.10", "3.11", "3.12"]

    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: Run tests
        run: pytest

Reference: See assets/matrix-build.yml

Workflow Best Practices

  1. Use specific action versions (@v4, not @latest)
  2. Cache dependencies to speed up builds
  3. Use secrets for sensitive data
  4. Implement status checks on PRs
  5. Use matrix builds for multi-version testing
  6. Set appropriate permissions
  7. Use reusable workflows for common patterns
  8. Implement approval gates for production
  9. Add notification steps for failures
  10. Use self-hosted runners for sensitive workloads

Reusable Workflows

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

on:
  workflow_call:
    inputs:
      node-version:
        required: true
        type: string
    secrets:
      NPM_TOKEN:
        required: true

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
      - run: npm test

Use reusable workflow:

yaml
jobs:
  call-test:
    uses: ./.github/workflows/reusable-test.yml
    with:
      node-version: "20.x"
    secrets:
      NPM_TOKEN: ${{ secrets.NPM_TOKEN }}

Security Scanning

yaml
name: Security Scan

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

jobs:
  security:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Run Trivy vulnerability scanner
        uses: aquasecurity/trivy-action@0.28.0
        with:
          scan-type: "fs"
          scan-ref: "."
          format: "sarif"
          output: "trivy-results.sarif"

      - name: Upload Trivy results to GitHub Security
        uses: github/codeql-action/upload-sarif@v3
        with:
          sarif_file: "trivy-results.sarif"

      - name: Run Snyk Security Scan
        uses: snyk/actions/node@0.4.0
        env:
          SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}

Deployment with Approvals

yaml
name: Deploy to Production

on:
  push:
    tags: ["v*"]

jobs:
  deploy:
    runs-on: ubuntu-latest
    environment:
      name: production
      url: https://app.example.com

    steps:
      - uses: actions/checkout@v4

      - name: Deploy application
        run: |
          echo "Deploying to production..."
          # Deployment commands here

      - name: Notify Slack
        if: success()
        uses: slackapi/slack-github-action@v1
        with:
          webhook-url: ${{ secrets.SLACK_WEBHOOK }}
          payload: |
            {
              "text": "Deployment to production completed successfully!"
            }

Related Skills

  • gitlab-ci-patterns - For GitLab CI workflows
  • deployment-pipeline-design - For pipeline architecture
  • secrets-management - For secrets handling

Frequently asked questions

What does the Github Actions Templates AI skill do?

Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.

Why use Github Actions Templates on TypingMind?

Because you install it once and use it with any model. Github Actions Templates 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 Github Actions Templates in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/cicd-automation/skills/github-actions-templates. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Github Actions Templates?

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 Github Actions Templates?

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

Is the Github Actions Templates AI skill free?

Yes. It is published on GitHub by wshobson 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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