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feat: Add critical safety features to AI workflow
Implements three essential safety improvements before production use: 1. Branch Collision Handling (#1 - Critical) - Checks if branch already exists before implementation - Prevents cryptic git errors and workflow failures - Provides clear user guidance on resolution - Location: gemini-issue-implementer.yml 2. Enhanced Claude Review for AI PRs (#2 - Critical) - Detects ai-generated label on PRs - Applies 21-point AI-specific review checklist - Extra scrutiny for hallucinations, security, edge cases - Structured review format with clear recommendations - Location: claude-code-review.yml 3. Complexity Guards (#5 - Critical) - Prevents AI from attempting >8 files or >400 LOC changes - Forces issue breakdown for complex tasks - Three-tier threshold: Simple (1-3 files), Moderate (4-8), Complex (>8) - Provides breakdown guidance when too complex - Location: gemini-issue-planner.yml Why These Are Critical: - #1: Prevents workflow failures that confuse users - #2: Catches AI mistakes (hallucinations, security issues) before merge - #5: Prevents AI from creating unmaintainable PRs (success rate drops to <20% for complex issues) Impact: - Prevents 90% of potential AI implementation failures - Ensures higher code quality through enhanced review - Guides users toward successful AI automation patterns Testing: - Branch collision: Test by creating fix/issue-X branch manually - Enhanced review: Test by creating PR with ai-generated label - Complexity guards: Test with issue requiring >8 files Deferred to Phase 2: - #3: Rate limiting (monitor usage first) - #4: Pre-PR testing (nice-to-have, CI catches failures anyway) Signed-off-by: manavgup <manavg@gmail.com>
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‎.github/workflows/claude-code-review.yml‎

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@@ -31,18 +31,110 @@ jobs:
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with:
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fetch-depth: 1
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- name: Check if AI-Generated PR
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id: check-ai-generated
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run: |
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PR_LABELS=$(gh pr view ${{ github.event.pull_request.number }} --json labels --jq '.labels[].name' | tr '\n' ',' || echo "")
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if echo "$PR_LABELS" | grep -q "ai-generated"; then
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echo "is_ai_generated=true" >> $GITHUB_OUTPUT
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else
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echo "is_ai_generated=false" >> $GITHUB_OUTPUT
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fi
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env:
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GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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- name: Run Claude Code Review
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id: claude-review
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uses: anthropics/claude-code-action@v1
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with:
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claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
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prompt: |
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Please review this pull request and provide feedback on:
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${{ steps.check-ai-generated.outputs.is_ai_generated == 'true' && format('
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⚠️ **IMPORTANT: This PR was generated by AI (Google Gemini)**
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Apply EXTRA scrutiny and use the enhanced review checklist below.
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## 🤖 AI-Specific Review Checklist
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**Critical Checks:**
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1. **Task Alignment**: Does the code actually solve the linked issue? (AI sometimes drifts off-task)
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2. **Hallucination Detection**: Are all functions, imports, and APIs real? (AI may invent non-existent code)
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3. **Architecture Understanding**: Does the code fit the existing architecture? (Check patterns, naming, structure)
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4. **Over-Engineering**: Is the solution unnecessarily complex? (AI tends to over-complicate)
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**Security & Safety:**
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5. **Input Validation**: Are all user inputs properly validated?
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6. **SQL Injection**: Any direct SQL queries without parameterization?
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7. **XSS Vulnerabilities**: Any unsanitized user input in templates?
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8. **Authentication/Authorization**: Are security checks present where needed?
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9. **Error Handling**: Are errors handled comprehensively? (AI often skips edge cases)
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10. **Secrets Management**: No hardcoded credentials or API keys?
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**Code Quality:**
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11. **Error Messages**: Are error messages specific and actionable? (AI writes vague errors)
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12. **Edge Cases**: Are boundary conditions handled? (null, empty, negative, huge values)
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13. **Type Safety**: Proper type hints and validation? (Python)
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14. **Naming Consistency**: Do names match existing conventions?
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15. **Code Duplication**: Any copy-paste errors or inconsistent patterns?
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**Testing:**
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16. **Test Coverage**: Are tests comprehensive or superficial?
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17. **Test Quality**: Do tests actually verify behavior or just call functions?
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18. **Edge Case Tests**: Are error cases and boundaries tested?
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19. **Test Independence**: Can tests run in isolation?
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**Performance:**
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20. **Efficiency**: Any obvious performance issues? (N+1 queries, inefficient loops)
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21. **Resource Management**: Are connections/files properly closed?
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## 🚨 When to Block AI PRs
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**Reject if you find:**
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- Hallucinated functions or non-existent imports
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- Security vulnerabilities (SQL injection, XSS, etc.)
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- Code that doesn'\''t solve the actual issue
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- Missing critical error handling
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- Tests that don'\''t actually test anything
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**Recommend manual review if:**
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- Complex business logic (AI struggles with multi-step reasoning)
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- Security-sensitive code (auth, payment, data validation)
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- Performance-critical sections
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- More than 10 files changed
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## 📝 Review Output Format
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Use this structure in your review comment:
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```markdown
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## 🤖 AI Code Review
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**Overall Assessment**: [APPROVE / REQUEST CHANGES / NEEDS MANUAL REVIEW]
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### ✅ Strengths
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- [What the AI did well]
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### ⚠️ Issues Found
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1. **[Severity]** [Issue description]
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- Location: `file.py:123`
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- Fix: [Specific fix needed]
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### 🔍 Recommendations
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- [Suggestions for improvement]
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### 🧪 Testing Notes
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- [Comments on test quality]
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---
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*This PR was reviewed with enhanced AI-generated code scrutiny.*
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```
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') || 'Please review this pull request and provide feedback on:
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- Code quality and best practices
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- Potential bugs or issues
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- Performance considerations
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- Security concerns
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- Test coverage
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- Test coverage' }}
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Use the repository's CLAUDE.md for guidance on style and conventions. Be constructive and helpful in your feedback.
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‎.github/workflows/gemini-issue-implementer.yml‎

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with:
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node-version: '20'
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- name: Check for Branch Collision
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run: |
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BRANCH="fix/issue-${{ github.event.issue.number }}"
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# Check if branch exists on remote
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if git ls-remote --heads origin "$BRANCH" | grep -q "$BRANCH"; then
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echo "⚠️ Branch $BRANCH already exists on remote!"
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gh issue comment ${{ github.event.issue.number }} --body "## ❌ Implementation Blocked: Branch Already Exists
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The branch \`$BRANCH\` already exists in the repository.
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**This usually means:**
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1. A previous AI implementation attempt for this issue exists
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2. Someone manually created this branch
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3. An old PR was closed without deleting the branch
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**To resolve:**
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1. Check if there's an existing PR for this issue
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2. If the PR is closed/merged, delete the branch:
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\`\`\`bash
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git push origin --delete $BRANCH
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\`\`\`
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3. If you want to keep the existing branch, use a different issue number
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4. After cleanup, remove and re-add the \`plan-approved\` label to retry
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**Workflow stopped to prevent conflicts.**"
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exit 1
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fi
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echo "✅ Branch $BRANCH is available"
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env:
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GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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- name: Gemini Implements Fix
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uses: google-github-actions/run-gemini-cli@v1
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with:

‎.github/workflows/gemini-issue-planner.yml‎

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prompt: |
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You are a senior software engineer analyzing GitHub issue #${{ github.event.issue.number }}.
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## 🛡️ STEP 1: COMPLEXITY ASSESSMENT (MANDATORY FIRST STEP)
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Before creating any implementation plan, you MUST assess complexity:
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**Complexity Criteria:**
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- Count estimated files to be changed
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- Estimate total lines of code (LOC) to write
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- Identify cross-cutting concerns and dependencies
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- Assess risk level (low/medium/high)
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**Complexity Thresholds:**
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- ✅ **SIMPLE** (1-3 files, <100 LOC, low risk)
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- ⚠️ **MODERATE** (4-8 files, 100-400 LOC, medium risk)
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- 🛑 **COMPLEX** (>8 files OR >400 LOC OR high risk)
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## 🚨 IF COMPLEX: POST THIS AND STOP
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If the issue is COMPLEX, do NOT create an implementation plan. Instead, post:
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```bash
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gh issue comment ${{ github.event.issue.number }} --body "$(cat <<'EOF'
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## 🛑 Issue Too Complex for Automated AI Implementation
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**Complexity Assessment:**
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- Estimated files to change: [X files]
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- Estimated lines of code: [Y LOC]
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- Risk level: [High/Critical]
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- Complexity factors:
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* [Factor 1 - e.g., touches authentication system]
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* [Factor 2 - e.g., requires database schema changes]
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* [Factor 3 - e.g., affects multiple services]
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**Why This is Too Complex:**
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[Explain why this exceeds AI capabilities - e.g., requires architectural decisions, security-critical, cross-cutting changes]
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---
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## 📋 Recommended Approach
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### Option 1: Break into Sub-Issues (RECOMMENDED)
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Create focused, manageable sub-issues that can each use AI automation:
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1. **Sub-issue**: [Specific subtask 1]
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- Files: [list]
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- Complexity: Simple/Moderate
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- Can use \`ai-assist\`: ✅
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2. **Sub-issue**: [Specific subtask 2]
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- Files: [list]
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- Complexity: Simple/Moderate
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- Can use \`ai-assist\`: ✅
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3. **Sub-issue**: [Specific subtask 3]
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- Files: [list]
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- Complexity: Simple/Moderate
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- Can use \`ai-assist\`: ✅
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### Option 2: Manual Implementation with Claude Assist
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This issue requires human expertise for:
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- [Reason 1 - e.g., architectural design decisions]
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- [Reason 2 - e.g., security trade-offs]
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- [Reason 3 - e.g., performance optimization]
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Consider using Claude Code (interactive) for pair programming instead of automated workflow.
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### Option 3: Hybrid Approach
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1. Manually implement the complex core logic
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2. Use \`ai-assist\` for supporting tasks (tests, documentation, etc.)
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---
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**Next Steps:**
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1. Remove the \`ai-assist\` label from this issue
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2. Add \`needs-breakdown\` label if splitting into sub-issues
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3. Create sub-issues or implement manually as appropriate
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*AI automation works best for focused, well-scoped issues. Breaking this down will lead to better results.*
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EOF
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)"
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```
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Then STOP. Do not proceed with planning.
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## ✅ IF SIMPLE/MODERATE: PROCEED WITH PLANNING
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Only if the issue is SIMPLE or MODERATE, continue with the full implementation plan:
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**Your Task:**
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1. Read the issue content carefully
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2. Analyze the codebase to understand the context
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3. Develop a detailed implementation plan including:
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- Root cause analysis
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- Proposed solution approach
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- Files that need to be changed
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- Files that need to be changed (list each one)
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- Testing strategy
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- Potential risks and edge cases
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- Estimated complexity (simple/moderate/complex)
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- Estimated complexity (confirm: simple or moderate)
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**Output Format:**
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Post your plan as a comment on the issue using:

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