GitHub Actions Integration

name: AI Code Review
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4
        with:
          fetch-depth: 0  # Fetch full history for better context

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '18'

      - name: Run AI Code Review
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx --quiet << 'EOF'
          Please review the changes in this pull request. Focus on:
          1. Code quality and best practices
          2. Potential bugs or security issues
          3. Performance considerations
          4. Documentation completeness
          
          Provide a summary of your findings and any recommendations.
          EOF

name: Update Documentation
on:
  push:
    branches: [main]
    paths: ['src/**', 'lib/**']

jobs:
  update-docs:
    runs-on: ubuntu-latest
    permissions:
      contents: write
      pull-requests: write
    
    steps:
      - uses: actions/checkout@v4
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
      
      - name: Generate Documentation
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx << 'EOF'
          Please update the documentation based on recent code changes:
          
          1. Review the git diff since the last documentation update
          2. Update README.md with any new features or API changes
          3. Generate or update API documentation for modified functions
          4. Ensure all examples are current and working
          5. Update the changelog with a summary of changes
          EOF
      
      - name: Create Pull Request
        uses: peter-evans/create-pull-request@v5
        with:
          title: "docs: automated documentation update"
          body: "Automated documentation update based on recent code changes"
          branch: docs/auto-update

name: Code Quality Check
on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  quality-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      
      - name: Analyze Code Quality
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx << 'EOF'
          Perform a comprehensive code quality analysis:
          
          1. Check for code smells and anti-patterns
          2. Identify potential performance bottlenecks
          3. Review error handling and edge cases
          4. Assess test coverage and suggest missing tests
          5. Verify adherence to project coding standards
          
          Generate a detailed report with specific recommendations.
          Save the report to quality-report.md
          EOF
      
      - name: Upload Quality Report
        uses: actions/upload-artifact@v4
        with:
          name: quality-report
          path: quality-report.md
      
      - name: Comment on PR
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            if (fs.existsSync('quality-report.md')) {
              const report = fs.readFileSync('quality-report.md', 'utf8');
              github.rest.issues.createComment({
                issue_number: context.issue.number,
                owner: context.repo.owner,
                repo: context.repo.repo,
                body: `## 🤖 AI Code Quality Report\n\n${report}`
              });
            }

name: Security Audit
on:
  schedule:
    - cron: '0 2 * * 1'  # Weekly on Monday at 2 AM
  workflow_dispatch:

jobs:
  security-audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Security Analysis
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx << 'EOF'
          Conduct a security audit of the codebase:
          
          1. Scan for common security vulnerabilities (OWASP Top 10)
          2. Check for hardcoded secrets or sensitive data
          3. Review authentication and authorization mechanisms
          4. Analyze input validation and sanitization
          5. Check for insecure dependencies
          6. Review API endpoints for security best practices
          
          Generate a security report with findings and remediation steps.
          Save the report to security-audit.md
          EOF
      
      - name: Create Security Issue
        if: always()
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            if (fs.existsSync('security-audit.md')) {
              const report = fs.readFileSync('security-audit.md', 'utf8');
              github.rest.issues.create({
                owner: context.repo.owner,
                repo: context.repo.repo,
                title: `🔒 Weekly Security Audit - ${new Date().toISOString().split('T')[0]}`,
                body: report,
                labels: ['security', 'audit']
              });
            }

name: Custom Agent Analysis
on:
  pull_request:

jobs:
  custom-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Run Custom Agent
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx --agent .qckfx/agents/code-reviewer.json << 'EOF'
          Review this pull request for code quality and suggest improvements.
          EOF

name: Multi-Agent Analysis
on:
  pull_request:

jobs:
  multi-agent:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Run Analysis with Sub-Agents
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          npx qckfx --with-subagent security --with-subagent performance << 'EOF'
          Analyze this codebase using security and performance sub-agents.
          Provide comprehensive recommendations for improvements.
          EOF

name: Comprehensive AI Analysis
on:
  pull_request:

jobs:
  ai-analysis:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        analysis-type:
          - security
          - performance
          - documentation
          - testing
    
    steps:
      - uses: actions/checkout@v4
      
      - name: Run ${{ matrix.analysis-type }} Analysis
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        run: |
          case "${{ matrix.analysis-type }}" in
            security)
              PROMPT="Focus on security vulnerabilities and best practices"
              ;;
            performance)
              PROMPT="Analyze performance bottlenecks and optimization opportunities"
              ;;
            documentation)
              PROMPT="Review and improve code documentation and comments"
              ;;
            testing)
              PROMPT="Assess test coverage and suggest additional test cases"
              ;;
          esac
          
          npx qckfx << EOF
          $PROMPT
          
          Provide specific, actionable recommendations for this codebase.
          Save your analysis to ${{ matrix.analysis-type }}-report.md
          EOF
      
      - name: Upload Analysis Report
        uses: actions/upload-artifact@v4
        with:
          name: ${{ matrix.analysis-type }}-report
          path: ${{ matrix.analysis-type }}-report.md

# Basic usage
npx qckfx "Your prompt here"

# Use specific model
npx qckfx --model claude-3-5-sonnet-20241022 "Analyze this code"

# Use custom agent configuration
npx qckfx --agent my-agent.json "Review the codebase"

# Override API credentials
npx qckfx --api-key YOUR_API_KEY "Analyze security"
npx qckfx --url https://custom-api.com/v1 "Review code"

# Continue previous session
npx qckfx --continue "Follow up on previous analysis"

# Add sub-agent tools
npx qckfx --with-subagent security performance "Comprehensive analysis"

# Validate agent configuration
npx qckfx --validate my-agent.json

# Quiet mode for CI/CD (recommended for GitHub Actions)
npx qckfx --quiet "Clean output for automated workflows"
npx qckfx -q --model claude-3-haiku-20240307 "Fast analysis with minimal output"

# ✅ Recommended for GitHub Actions
npx qckfx --quiet "Analyze this pull request for security issues"

# ❌ Not recommended for CI/CD (too verbose)
npx qckfx "Analyze this pull request for security issues"

env:
  LLM_API_KEY: ${{ secrets.LLM_API_KEY }}

npx qckfx --quiet --model gemini-2.5-pro  # Default, balanced performance
npx qckfx --quiet --model claude-3-haiku-20240307  # Faster, cheaper
npx qckfx --quiet --model claude-3-5-sonnet-20241022  # Better quality

npx qckfx << 'EOF'
Review only the files changed in this PR for:
1. TypeScript type safety issues
2. React component best practices
3. Performance implications

Ignore formatting and style issues.
EOF

npx qckfx << 'EOF'
Focus your analysis on the src/components directory.
Ignore node_modules, dist, and test files.
EOF

- name: Initial Analysis
  run: npx qckfx "Analyze the codebase architecture"

- name: Follow-up Analysis
  run: npx qckfx --continue "Based on the previous analysis, suggest refactoring opportunities"

# Ensure the secret is properly set
env:
  LLM_API_KEY: ${{ secrets.LLM_API_KEY }}

# Use a larger runner for large repositories
runs-on: ubuntu-latest-4-cores

# Add delays between API calls
- name: Wait between analyses
  run: sleep 30

# Check available models for your provider
npx qckfx --model gemini-2.5-pro "test prompt"

npx qckfx --help

npx qckfx --validate .qckfx/agents/my-agent.json

- name: Run Tests
  run: npm test

- name: AI Test Analysis
  env:
    LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
  run: |
    npx qckfx << 'EOF'
    Analyze the test results and suggest:
    1. Additional test cases for better coverage
    2. Improvements to existing tests
    3. Performance optimizations for the test suite
    EOF

- name: Deploy if Analysis Passes
  if: success()
  run: npm run deploy

# .github/workflows/reusable-ai-review.yml
name: Reusable AI Review
on:
  workflow_call:
    inputs:
      focus-area:
        required: true
        type: string
    secrets:
      llm-api-key:
        required: true

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: AI Review
        env:
          LLM_API_KEY: ${{ secrets.llm-api-key }}
        run: |
          npx qckfx << EOF
          Focus on ${{ inputs.focus-area }} in this codebase.
          Provide specific recommendations and examples.
          EOF