GitHub Actions Scaffolder

You are scaffolding a traditional GitHub Actions YAML workflow — deterministic CI/CD automation with no AI at runtime. This is different from agentic workflows.

When to Use This Skill vs Others

Task	Use This Skill	Use create-agentic-workflow
Run tests on every PR	✅	❌
Build and publish a Docker image	✅	❌
Deploy to GitHub Pages	✅	❌
Check if PR matches the spec	❌	✅
Daily repo health report	❌	✅
Code review with AI judgment	❌	✅

Execution Steps

1. Gather Requirements

Ask the user for the following context:

1. Workflow Category: What does this workflow need to do?

   • Test — run unit/integration tests on PR/push (pytest, jest, go test, etc.)
   • Build — compile, bundle, or build Docker images
   • Lint — run linters or formatters (ruff, eslint, markdownlint, etc.)
   • Deploy — publish to GitHub Pages, Vercel, AWS, etc.
   • Release — create GitHub releases, publish npm/PyPI packages
   • Security — dependency audits, SAST, secret scanning (CodeQL, trivy, etc.)
   • Maintenance — scheduled jobs, stale issue cleanup, dependency updates
   • Custom — describe the steps manually

2. Platform/Language: What stack? (Python, Node.js, Go, Docker, .NET, etc.)

3. Trigger Events: When should this fire?

   • pull_request — on PR open/update (most quality gates)
   • push to main — on merge to main (post-merge validation, deploys)
   • workflow_dispatch — manual run
   • schedule — cron schedule (maintenance jobs)
   • release — on GitHub Release published

2. Generate the Workflow

Run the scaffold script:

python ./scripts/scaffold_github_action.py \
  --skill-dir <path-to-skill-directory> \
  --category <test|build|lint|deploy|release|security|maintenance|custom> \
  --platform <python|nodejs|go|docker|dotnet|generic> \
  [--triggers pull_request push schedule workflow_dispatch] \
  [--name "My Workflow Name"] \
  [--branch main]

The script outputs a ready-to-use .yml file in .github/workflows/.

3. Post-Scaffold Guidance

After generating, advise the user:

• Platform-specific secrets: Some steps require repository secrets (e.g., PYPI_TOKEN, NPM_TOKEN, DOCKER_PASSWORD, DEPLOY_KEY).
• Pinned action versions: All generated steps use pinned @v4/@v3 action refs for security.
• Permissions: Generated workflows declare minimal permissions (contents: read by default, elevated only when needed).
• Review before committing: Treat workflow YAML as code — review it before merging.

4. Optional Optimization Loop (Autoresearch-Compatible)

If the workflow prompt/instructions need iterative calibration:

1. Capture a baseline eval first.
2. Change one dominant variable per iteration.
3. Label each attempt as keep/discard.
4. Log crashes/timeouts explicitly.
5. Preserve an iteration ledger in evals/results.tsv.

GitHub Actions Key Reference

Available Trigger Events

Trigger	Fires when	Common for
pull_request	PR opened/updated	Tests, lint, security
push	Branch pushed	Deploy, release checks
schedule (cron)	On a time schedule	Maintenance, reports
workflow_dispatch	Manual button click	Deploys, one-off jobs
release	Release published	Package publishing
issues	Issue opened/labeled	Triage, notifications
workflow_call	Called by another workflow	Reusable sub-workflows

Permissions Model

permissions:
  contents: read      # Read repo files
  contents: write     # Commit files, push
  pull-requests: write # Comment on PRs
  issues: write       # Create/update issues
  packages: write     # Publish packages
  id-token: write     # OIDC (for cloud deploys)

Always declare minimum required permissions. The GITHUB_TOKEN grants no permissions by default unless declared.

Common Action Patterns

# Checkout
- uses: actions/checkout@v4

# Setup language
- uses: actions/setup-python@v5
  with:
    python-version: "3.12"

# Cache dependencies
- uses: actions/cache@v4
  with:
    path: ~/.cache/pip
    key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}

# Upload artifacts
- uses: actions/upload-artifact@v4
  with:
    name: report
    path: output/

# Publish GitHub Release
- uses: softprops/action-gh-release@v2
  with:
    files: dist/*

Next Actions

• Continuous Improvement: Run ./scripts/benchmarking/run_loop.py --results-dir evals/experiments for controlled trigger optimization.
• Review Loop: Run ./scripts/eval-viewer/generate_review.py to inspect quality across iterations.
• Audit: Offer to run audit-plugin to validate the generated artifacts.