github-workflow-automation
About github-workflow-automation
A skill offering guidance for automating GitHub workflows with AI, for teams working on reviews, issue triage, CI/CD, and Git operations. It emphasizes safeguards and validation; its examples are partial designs, not ready-to-enable workflows.
Key features
- 01AI-assisted pull request review
- 02Issue triage automation patterns
- 03GitHub Actions workflow guidance
- 04Git operation automation patterns
- 05Security and validation safeguards
Use cases
- 01Designing automated pull request reviews
- 02Planning issue triage workflows
- 03Adding AI to CI/CD pipelines
- 04Automating rebases or cherry-picks
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ sickn33/ agentic-awesome-skills --skill github-workflow-automationAfter installing, type/github-workflow-automationin Claude Code to confirm it loaded.
Bundled filesreferences/detailed-guide.md
Details
From the project README🔧 GitHub Workflow Automation
Patterns for automating GitHub workflows with AI assistance, inspired by Gemini CLI and modern DevOps practices.
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use This Skill
Use this skill when:
- Automating PR reviews with AI
- Setting up issue triage automation
- Creating GitHub Actions workflows
- Integrating AI into CI/CD pipelines
- Automating Git operations (rebases, cherry-picks)
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Trust boundaries and missing implementations
These are partial design sketches, not ready-to-enable workflows: analyzeIssue/callAI/ai/execFile and the provider SDK setup are not supplied, example model/action versions are historical, multiline-output handling is incomplete, and deploy/rebase/rollback are deliberately disabled until concrete safeguards exist. Obtain authorization before enabling comments, labels, stale closure, schedules, branch pushes or protection changes. Never interpolate issue/comment/diff/file/branch text into shell or JavaScript source; pass data via environment or private files and use argument arrays with validated full SHAs. Use NUL-delimited Git filenames, random output delimiters or bounded artifact files instead of fixed EOF/multiline one-line outputs. Treat model inputs/results as untrusted; validate structured labels and bounded markdown and never let prompt text authorize tools or approve a PR. Do not execute fork/PR code, package scripts or checked-out actions in a secret/write-token context; use a trusted isolated reviewer, minimal permissions, pinned reviewed action SHAs and restricted network/data transfer. Public/fork PRs normally lack provider secrets/write tokens: fail/skip honestly rather than switching to an unsafe pull_request_target checkout. Check actual maintainer permission via GitHub API, exact command syntax, same-repo branch ownership and current head before any rebase; use an explicit expected-head lease and report conflicts without forceful resolution. Highest version tag is not last successful deployment, AI risk scores do not satisfy deterministic gates, and commit summaries do not describe all deploy changes. Preserve existing protection/ruleset requirements instead of replacing them with an example context list. Unknown changed paths/config/lockfile changes must trigger the full relevant test set; path selection alone is not dependency analysis.