ai-outfitter/actions: Automate AI Agents with GitHub Actions

Introduction

The ai-outfitter/actions GitHub Action provides a powerful way to integrate AI agents, powered by Outfitter, directly into your GitHub workflows. It allows you to run Outfitter profiles in a headless print mode, enabling agents to perform single units of work per workflow run and then exit. This action is designed to transform your GitHub repositories into dynamic environments where AI agents can automate a wide range of tasks.

Why Use and Benefits

By leveraging ai-outfitter/actions, developers can build sophisticated, event-driven, or scheduled AI agents directly within their GitHub repositories. This opens up possibilities for:

• Automated Code Review: Configure agents to review commits on a cron schedule or when a pull request is marked ready_for_review, providing early feedback before human intervention.
• Security Auditing: Automatically audit changes to sensitive paths, such as infra/, auth/, or migration files, ensuring compliance and security.
• Task Automation: Assign issues or pull requests to a bot account, and have the agent complete the task and push a PR, streamlining development workflows.
• On-Demand Operations: Trigger agent-implemented PRs or tasks on demand using workflow_dispatch, allowing for flexible automation.
• Preview Environment Reviews: Utilize browser access to review deployed preview environments, enabling comprehensive checks by AI agents.

The action provides a flexible framework for defining agent behavior, sourcing profiles from catalogs, and integrating with various model providers, making it a versatile tool for modern development practices.

Installation

To get started, you can add the ai-outfitter/actions to your GitHub workflow. Here's a quick example for an agent review on pull request readiness:

# .github/workflows/pr-review.yml
name: Agent review
on:
  pull_request:
    types: [ready_for_review]

permissions:
  contents: read
  pull-requests: write # let the agent comment on the PR

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - uses: ai-outfitter/actions@v1
        with:
          agent: reviewer
          source: my-org/outfitter-catalog
          source-ref: v1.2.0
          prompt: >-
            Review pull request #${{ github.event.pull_request.number }} in
            ${{ github.repository }}. Use `gh pr diff` and `gh pr view` to read
            it, then post your findings as a PR comment with `gh pr comment`.
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

Remember to configure your model provider credentials as repository or organization secrets.

Examples

The repository includes several examples to illustrate various use cases:

• scheduled-commit-review.yml: Cron-based review of recent commits.
• review-undrafted-pr.yml: Review when a PR leaves draft status.
• path-audit.yml: Audit pushes to specific directories.
• assigned-task-agent.yml: Complete work when an issue/PR is assigned to a bot account.
• pull-request-implementation.yml: Start or continue an agent PR on workflow_dispatch.
• issue-triage-dispatch.yml: Triage new issues and hand them off to implementation workflows.
• preview-environment-review.yml: Review a PR's deployed preview environment in a real browser.

These examples provide practical templates for integrating ai-outfitter/actions into your development lifecycle.

Links

• GitHub Repository: https://github.com/ai-outfitter/actions