example_workflow_gemini.yml

name: AI Slop Gate LLM GEMINI Analysis

on:
  pull_request:
    branches: [ main ]
  push:
    branches: [ main ]
  workflow_dispatch:

permissions:
  pull-requests: write
  contents: read

jobs:
  llm-analysis:
    runs-on: ubuntu-22.04
    timeout-minutes: 20

    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Cache ai-slop-gate cache directory
        uses: actions/cache@v4
        with:
          path: ~/.cache/ai-slop-gate
          key: ai-slop-gate-cache-${{ runner.os }}-${{ hashFiles('**/*.py', '**/*.yml', '**/*.yaml') }}
          restore-keys: |
            ai-slop-gate-cache-${{ runner.os }}-

      # Run LLM analysis
      - name: LLM Analysis (ai-slop-gate)
        id: llm_gate
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
        continue-on-error: true
        run: |
          mkdir -p ~/.cache/ai-slop-gate
          
          # Check if API key is available
          if [ -z "$GEMINI_API_KEY" ]; then
            echo "⚠️ GEMINI_API_KEY not set, skipping LLM analysis"
            echo "verdict=SKIPPED" >> $GITHUB_OUTPUT
            echo "findings=0" >> $GITHUB_OUTPUT
            exit 0
          fi
          
          # Check if policy.yml exists, otherwise use default
          POLICY_FLAG=""
          if [ -f "${{ github.workspace }}/policy.yml" ]; then
            echo "📋 Using custom policy.yml"
            POLICY_FLAG="--policy /data/policy.yml"
          else
            echo "📋 Using default policy"
          fi
          
          # Run LLM analysis and capture output
          set +e  # Disable exit on error temporarily
          docker run --rm \
            -v "${{ github.workspace }}:/data" \
            -v ~/.cache/ai-slop-gate:/root/.cache/ai-slop-gate \
            -e GITHUB_TOKEN \
            -e GEMINI_API_KEY \
            ghcr.io/sergudo/ai-slop-gate:latest \
            run --provider gemini --llm-local $POLICY_FLAG --path /data > raw_report.txt 2>&1
          
          EXIT_CODE=$?
          set -e  # Re-enable exit on error
          
          # Always show report
          cat raw_report.txt
          
          # Save exit code
          echo "exit_code=$EXIT_CODE" >> $GITHUB_OUTPUT
          
          # Extract verdict (default to UNKNOWN if not found)
          VERDICT=$(grep "Policy Verdict:" raw_report.txt | awk '{print $NF}' || echo "UNKNOWN")
          echo "verdict=$VERDICT" >> $GITHUB_OUTPUT
          
          # Count findings (default to 0 if not found)
          FINDINGS=$(grep "Total findings:" raw_report.txt | awk '{print $NF}' || echo "0")
          echo "findings=$FINDINGS" >> $GITHUB_OUTPUT
          
          # Log extracted values
          echo "📊 Extracted values:"
          echo "   Exit code: $EXIT_CODE"
          echo "   Verdict: $VERDICT"
          echo "   Findings: $FINDINGS"
          
          # Don't fail here
          exit 0

      # Post comment on PR (always)
      - name: Post LLM Analysis Report to PR
        if: github.event_name == 'pull_request' && always()
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          # Check if analysis was skipped
          if [ "${{ steps.llm_gate.outputs.verdict }}" = "SKIPPED" ]; then
            cat > final_comment.md << EOF
          ## ⏭️ AI Slop Gate LLM Analysis
          
          **Status:** SKIPPED  
          **Reason:** GEMINI_API_KEY not configured
          
          To enable LLM analysis, add \`GEMINI_API_KEY\` to repository secrets.
          
          <sub>🤖 Powered by [AI Slop Gate](https://github.com/SergUdo/ai-slop-gate) | Run: \`${{ github.run_id }}\`</sub>
          EOF
            
            gh pr comment ${{ github.event.pull_request.number }} \
              --body-file final_comment.md \
              --repo ${{ github.repository }}
            exit 0
          fi
          
          # Extract clean report
          sed -n '/=== AI SLOP GATE REPORT ===/,/=== END OF REPORT ===/p' raw_report.txt > clean_report.md
          
          # Check if report was extracted
          if [ ! -s clean_report.md ]; then
            echo "⚠️ Warning: Could not extract report from raw_report.txt"
            echo "=== NO REPORT GENERATED ===" > clean_report.md
            echo "The LLM analysis may have failed to run properly." >> clean_report.md
          fi
          
          # Get values with defaults
          VERDICT="${{ steps.llm_gate.outputs.verdict }}"
          FINDINGS="${{ steps.llm_gate.outputs.findings }}"
          
          # Set defaults if empty
          VERDICT="${VERDICT:-UNKNOWN}"
          FINDINGS="${FINDINGS:-0}"
          
          echo "📊 Report values:"
          echo "   Verdict: $VERDICT"
          echo "   Findings: $FINDINGS"
          
          # Determine emoji and status
          if [ "$VERDICT" = "BLOCKING" ]; then
            EMOJI="🚨"
            STATUS="**BLOCKING** - Action Required"
          elif [ "$VERDICT" = "ADVISORY" ]; then
            EMOJI="⚠️"
            STATUS="**ADVISORY** - Review Recommended"
          elif [ "$VERDICT" = "ALLOW" ]; then
            EMOJI="✅"
            STATUS="**PASSED** - No Issues Found"
          else
            EMOJI="❓"
            STATUS="**UNKNOWN** - Check logs"
          fi
          
          # Create professional comment
          cat > final_comment.md << EOF
          ## $EMOJI AI Slop Gate LLM Analysis (Gemini)
          
          **Status:** $STATUS  
          **Findings:** $FINDINGS issue(s) detected
          
          > 🤖 **Deep Analysis:** This report uses AI to detect architectural issues, anti-patterns, and logic flaws that static analysis might miss.
          
          ---
          
          EOF
          
          # Append the clean report
          cat clean_report.md >> final_comment.md
          
          # Add footer with fix guide ONLY if there are violations
          if [ "$FINDINGS" != "0" ] && [ "$VERDICT" != "ALLOW" ]; then
            cat >> final_comment.md << EOF
          
          ---
          
          <details>
          <summary>📚 Understanding LLM Findings</summary>
          
          ### What LLM Analysis Detects
          - Architectural anti-patterns and code smells
          - Logic inconsistencies and contradictions
          - Misleading naming or documentation
          - Potential design flaws
          - Security vulnerabilities in business logic
          
          ### How to Interpret Findings
          - **High Confidence (0.8-1.0):** Strong evidence of an issue
          - **Medium Confidence (0.5-0.8):** Worth investigating
          - **Low Confidence (<0.5):** Consider in context
          
          ### False Positives
          LLM analysis may flag intentional design decisions. Review findings critically and validate against your requirements.
          
          </details>
          EOF
          fi
          
          # Always add footer
          cat >> final_comment.md << EOF
          
          <sub>🤖 Powered by [AI Slop Gate](https://github.com/SergUdo/ai-slop-gate) + Gemini | Run: \`${{ github.run_id }}\`</sub>
          EOF
          
          # Post comment
          gh pr comment ${{ github.event.pull_request.number }} \
            --body-file final_comment.md \
            --repo ${{ github.repository }}

      # Set job status based on verdict
      - name: Check LLM Analysis Result
        if: steps.llm_gate.outputs.verdict == 'BLOCKING'
        run: |
          echo "❌ LLM analysis found blocking violations"
          exit 1