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gh-aw

gh-aw: Define AI-powered repository automation in Markdown and run AI agents through GitHub Actions.

What problem it solves

Repository automation often needs reasoning or interpretation for tasks such as issue triage, pull-request review, CI failure investigation, documentation maintenance, dependency analysis, and repository reporting. The project provides a way to define these agentic workflows in Markdown with YAML frontmatter and compile them into standard GitHub Actions workflows while retaining explicit controls for permissions and writes.

Key features

  • Markdown workflow definitions — Workflow authors combine YAML frontmatter configuration with a Markdown body that describes the agent's task. Frontmatter controls triggers, permissions, tools, and the AI engine.

    Source: README

  • Standard GitHub Actions compilation — The `gh aw compile` command validates workflow source and generates the `.lock.yml` workflow that GitHub Actions executes.

    Source: README

  • Multiple built-in AI engines — Workflows can select among GitHub Copilot, Claude Code, OpenAI Codex, Google Gemini, and Pi as their AI engine.

    Source: README

  • Sandboxed and read-only agent jobs — Agent jobs use read-only GitHub access and sandboxed execution by default. Configured writes are buffered, validated, and applied by separate `safe-outputs` jobs with scoped permissions.

    Source: README

  • Complementary to conventional CI/CD — The project keeps deterministic builds, tests, linting, deployments, and reproducible scripts in conventional GitHub Actions, adding agentic workflows where interpretation is needed.

    Source: README

Quick start

Copied verbatim from the project's README.

gh extension install github/gh-aw

Who it may suit

This may suit developers and repository maintainers who need AI-assisted issue triage, pull-request review, CI investigation, documentation maintenance, dependency analysis, or repository reporting through GitHub Actions.

Topics to discuss

  • Which repository automation tasks are most suitable for agentic workflows rather than conventional CI/CD jobs?

  • How should workflow authors balance scoped permissions and network access with agent capabilities?

  • Which AI engine selection and safe-output patterns work best for different kinds of repository automation?


Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/github/gh-aw#readme

ReadmeX opened this community for a trending project; it is not run by the project. Maintainers can claim it from the community page.

github.comhttps://github.com/github/gh-aw

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