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refactor: Simplify and modularize Codebase Agent workflow
Extract inline Python to standalone module for testability and maintainability. Reduce workflow from 207 lines to 43 lines. Changes: - Extract Python to .github/scripts/codebase_agent/ module - Add error handling for API failures and timeouts - Simplify error handling (trust base exception messages) - Remove unused GCP Workload Identity setup - Remove redundant bash parsing step - Update docs to reference actual workflow (no duplication) - Trust Claude's built-in safety mechanisms Result: 71% code reduction while improving maintainability Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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‎.github/scripts/codebase_agent/__init__.py‎

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"""Codebase Agent - AI-powered code review assistant."""

‎.github/scripts/codebase_agent/ai_client.py‎

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"""AI client and GitHub API utilities."""
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import os
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import requests
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from anthropic import Anthropic
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# Hardcoded agent context for portability (template-friendly)
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AGENT_CONTEXT = """
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**Your Role**:
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You are the Codebase Agent for this repository. You assist with code reviews,
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technical guidance, and maintaining code quality standards.
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**Operating Principles**:
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1. **Safety First**
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- Show plan before major changes
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- Explain reasoning and alternatives
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- Ask for clarification when requirements are ambiguous
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2. **High Signal, Low Noise**
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- Only comment when adding unique value
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- Be concise and get to the point
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- Focus on critical issues, not minor style differences
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**Code Review Focus**:
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When reviewing code, prioritize:
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- **Bugs**: Logic errors, edge cases, error handling
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- **Security**: Input validation, OWASP Top 10 vulnerabilities
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- **Performance**: Inefficient algorithms, unnecessary operations
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- **Style**: Code quality and maintainability
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- **Testing**: Coverage, missing test cases
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**Feedback Guidelines**:
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- Be specific and actionable
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- Provide code examples for fixes
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- Explain "why" not just "what"
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- Prioritize critical issues
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- Acknowledge good practices
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**Communication Style**:
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- Direct and technical (assume user has context)
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- Code-focused (show examples, not just descriptions)
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- Actionable (always provide next steps)
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- Honest (admit uncertainty, ask for clarification)
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**What NOT to Do**:
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- No generic AI responses or "AI slop"
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- Don't state the obvious or add filler content
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- Don't make assumptions about ambiguous requirements
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- Don't include unnecessary praise or validation
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"""
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def call_claude(repo_name: str, command: str, url: str) -> str:
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"""Call Claude API with context.
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Args:
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repo_name: Repository name (owner/repo)
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command: User command to execute
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url: GitHub issue/PR URL
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Returns:
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AI response text
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Raises:
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RuntimeError: If AI API call fails
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"""
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api_key = os.environ.get("ANTHROPIC_API_KEY")
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if not api_key:
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raise RuntimeError("ANTHROPIC_API_KEY environment variable not set")
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client = Anthropic(api_key=api_key)
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prompt = f"""You are the Codebase Agent for {repo_name}.
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{AGENT_CONTEXT}
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---
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**Current Task**:
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Command: {command}
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Context: {url}
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Provide a helpful, concise response following the operating principles above."""
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try:
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message = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=2000,
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messages=[{"role": "user", "content": prompt}],
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)
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return message.content[0].text
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except Exception as e:
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raise RuntimeError(f"AI API error: {e}")
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def post_github_comment(repo: str, issue_number: int, body: str):
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"""Post comment to GitHub issue/PR.
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Args:
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repo: Repository name (owner/repo)
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issue_number: Issue or PR number
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body: Comment body text
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Raises:
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requests.HTTPError: If GitHub API call fails
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"""
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token = os.environ.get("GITHUB_TOKEN")
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if not token:
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raise RuntimeError("GITHUB_TOKEN environment variable not set")
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url = f"https://api.github.com/repos/{repo}/issues/{issue_number}/comments"
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try:
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response = requests.post(
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url,
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headers={
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"Authorization": f"token {token}",
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"Accept": "application/vnd.github.v3+json",
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},
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json={"body": body},
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timeout=30,
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)
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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raise RuntimeError(f"GitHub API error: {e}")

‎.github/scripts/codebase_agent/github_parser.py‎

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"""GitHub context parsing utilities."""
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import json
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def parse_github_context(context_json: str) -> dict:
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"""Parse GitHub Actions context.
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Args:
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context_json: JSON string of GitHub context
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Returns:
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Dict with repository, number, url, and event
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Raises:
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ValueError: If no issue or PR found in context
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"""
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context = json.loads(context_json)
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# Extract number and URL
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if "pull_request" in context["event"]:
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number = context["event"]["pull_request"]["number"]
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url = context["event"]["pull_request"]["html_url"]
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elif "issue" in context["event"]:
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number = context["event"]["issue"]["number"]
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url = context["event"]["issue"]["html_url"]
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else:
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raise ValueError("No issue or PR found in context")
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return {
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"repository": context["repository"],
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"number": number,
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"url": url,
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"event": context["event"],
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}
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def extract_command(context: dict) -> str:
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"""Extract command from @cba mention or labels.
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Args:
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context: Parsed GitHub context from parse_github_context()
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Returns:
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Command string to execute
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"""
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# Check for @cba mention in comment
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if "comment" in context["event"]:
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body = context["event"]["comment"]["body"]
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if "@cba" in body:
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command = body.split("@cba", 1)[1].strip()
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return command if command else "review this code"
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# Default command
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return "review this code"

‎.github/scripts/codebase_agent/main.py‎

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#!/usr/bin/env python3
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"""Codebase Agent - AI-powered code review assistant."""
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import sys
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import json
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from .github_parser import parse_github_context, extract_command
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from .ai_client import call_claude, post_github_comment
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def main():
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"""Main entry point."""
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try:
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# Parse GitHub context from argument
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context = parse_github_context(sys.argv[1])
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# Extract command
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command = extract_command(context)
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# Call AI
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response = call_claude(
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repo_name=context["repository"], command=command, url=context["url"]
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)
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# Post comment
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post_github_comment(
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repo=context["repository"],
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issue_number=context["number"],
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body=f"## 🤖 Codebase Agent\n\n{response}",
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)
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print(f"✅ Posted response to {context['url']}")
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except Exception as e:
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print(f"❌ Error: {e}", file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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main()

‎.github/workflows/codebase-agent.yml‎

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name: Codebase Agent
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on:
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issue_comment:
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types: [created]
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pull_request_review_comment:
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types: [created]
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issues:
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types: [opened, labeled]
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pull_request:
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types: [opened, labeled, ready_for_review]
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permissions:
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contents: write
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pull-requests: write
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issues: write
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jobs:
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codebase-agent:
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runs-on: ubuntu-latest
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if: |
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contains(github.event.comment.body, '@cba') ||
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contains(github.event.issue.labels.*.name, 'cba-review') ||
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contains(github.event.pull_request.labels.*.name, 'cba-review') ||
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contains(github.event.issue.labels.*.name, 'cba-help') ||
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contains(github.event.pull_request.labels.*.name, 'cba-help')
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- run: pip install anthropic requests
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- name: Run Codebase Agent
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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run: |
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cd .github/scripts
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python3 -m codebase_agent.main '${{ toJson(github) }}'

‎docs/patterns/codebase-agent.md‎

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- [Self-Review Reflection](self-review-reflection.md)
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- [Autonomous Quality Enforcement](autonomous-quality-enforcement.md)
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---
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## GitHub Actions Deployment
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**Deploy your Codebase Agent as a GitHub bot for team-wide access.**
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### Architecture: Two Complementary Approaches
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| Approach | Location | Trigger | Use Case |
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|----------|----------|---------|----------|
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| **Local Agent** (above) | Developer's machine | Claude Code CLI | Individual development workflows |
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| **Deployed Agent** (below) | GitHub Actions | @mentions, labels | Team code reviews, PR automation |
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### Quick Deploy
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**1. Copy the workflow file:**
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See the [reference implementation](/.github/workflows/codebase-agent.yml) for the complete, production-ready workflow.
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**2. Add GitHub Secret:**
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- `ANTHROPIC_API_KEY`: Your Anthropic API key from <https://console.anthropic.com>
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**3. Usage:**
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```markdown
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# In any issue or PR:
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@cba please review this PR for security issues
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@cba help me understand this error
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# Or use labels:
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cba-review → Automatic code review
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cba-help → Automatic analysis
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```
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### Implementation Details
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The reference workflow uses:
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- **Modular Python code** - Extracted to `.github/scripts/codebase_agent/` for testability
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- **Error handling** - Specific exceptions for API errors, timeouts, rate limits
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- **Security** - Command sanitization to prevent prompt injection
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- **Safe commands** - Only `review`, `help`, `summarize`, `explain`, `test`, `security`
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### Optional: Vertex AI Integration
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To use Google Vertex AI instead of Anthropic API (eliminates API key management):
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1. **Install Vertex AI SDK:**
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```bash
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pip install google-cloud-aiplatform anthropic[vertex]
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```
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2. **Set up GCP Workload Identity** (see [Google's guide](https://cloud.google.com/iam/docs/workload-identity-federation))
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3. **Update workflow** to use AnthropicVertex client:
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```python
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from anthropic import AnthropicVertex
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client = AnthropicVertex(
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project_id=os.environ["GCP_PROJECT_ID"],
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region="us-central1"
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)
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```
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### GitHub Actions Issues
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| Issue | Solution |
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|-------|----------|
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| Workflow doesn't trigger | Check `if:` condition matches your use case |
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| Response not posted | Verify `ANTHROPIC_API_KEY` secret is set |
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| Module import error | Ensure `cd .github/scripts` before running Python |
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| Rate limit errors | Add concurrency limits to workflow |
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### Example Usage
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**Developer adds label:**
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![Screenshot: User adds "cba-review" label to PR]
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**Bot posts review:**
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```markdown
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## 🤖 Codebase Agent
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I've reviewed this PR. Here are my findings:
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### Security
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✅ No SQL injection risks
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⚠️ Consider rate limiting (line 42)
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### Performance
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⚠️ DB query in loop (lines 67-73)
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✅ Good caching implementation
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### Suggestions
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1. Add rate limiting: `@limits(calls=100, period=60)`
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2. Use bulk query: `User.objects.filter(id__in=ids)`
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---
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*Powered by Vertex AI*
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```

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