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feature-verification

Systematic verification of newly implemented features using multi-layer testing. Use when verifying features work after implementation, debugging "should work but doesn't" issues, validating deployments, or ensuring code changes are correctly deployed and functional.

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abcnuts/manus-skills
ソースの最終更新活動
2026年2月12日 04:11
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SKILL.md
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name
feature-verification
description
Systematic verification of newly implemented features using multi-layer testing. Use when verifying features work after implementation, debugging "should work but doesn't" issues, validating deployments, or ensuring code changes are correctly deployed and functional.
license
Complete terms in LICENSE.txt
# Feature Verification Systematic 4-phase process for verifying new features work correctly across all layers: database, code deployment, runtime execution, and end-to-end functionality. ## When to Use - After implementing new features - When features "should work" but don't behave as expected - When verifying complex integrations - After deployments to production - When debugging deployment-related issues ## Core Methodology Follow these 4 phases in order. Each phase builds on the previous one. ### Phase 1: Root Cause Investigation Gather evidence from all layers before forming hypotheses. **Layer 1: Database Schema** - Verify tables and columns exist - Check data types and constraints - Confirm indexes and relationships - Test RLS policies **Layer 2: Code Deployment** - Verify all files are deployed - Check deployment version number - Confirm imports are correct - Validate environment variables **Layer 3: Runtime Execution** - Add logging to track execution paths - Verify functions are being called - Check async operations complete - Review error logs **Layer 4: End-to-End Testing** - Test complete user workflows - Verify data persistence - Check related features still work - Validate error handling **Detailed checklists:** See `references/verification-checklist.md` ### Phase 2: Pattern Analysis Compare expected vs actual behavior to identify patterns. **Questions to ask:** - What should happen vs what actually happens? - Are there similar working examples to compare against? - Is this issue consistent or intermittent? - What changed recently that might affect this? **Look for patterns:** - Configuration differences between working/broken features - Timing issues (async operations) - Permission/authentication issues - Deployment-specific problems ### Phase 3: Hypothesis and Testing Form testable hypotheses based on evidence, then test systematically. **Hypothesis template:** ``` Hypothesis: [Description of suspected root cause] Test: [How to verify this hypothesis] Evidence: [What evidence supports/refutes this] Conclusion: ✅ CONFIRMED / ❌ RULED OUT / ❓ INCONCLUSIVE ``` **Test each hypothesis:** - Start with most likely causes - Rule out possibilities systematically - Document evidence for each test - Don't skip to solutions without testing **Common root causes:** See `references/common-deployment-issues.md` ### Phase 4: Implementation and Verification Fix identified issues and verify the fixes work. **For each fix:** 1. Document what you're changing and why 2. Implement the fix 3. Redeploy if necessary 4. Retest the feature 5. Verify related features still work **Make fixes robust:** - Add logging for future debugging - Handle edge cases - Update documentation - Consider long-term improvements ## Deliverables Create a verification report documenting: 1. What was tested 2. Evidence gathered from each layer 3. Patterns identified 4. Hypotheses tested 5. Root causes found 6. Fixes implemented 7. Recommendations **Use template:** `templates/verification_report_template.md` ## Best Practices **Evidence Over Assumptions** - Gather data before forming theories - Document what works AND what doesn't - Test hypotheses systematically **Bottom-Up Verification** - Start with database (foundation) - Move to code deployment - Check runtime execution - Test end-to-end last **Work Backwards on Failures** - If end-to-end fails, check runtime - If runtime fails, check deployment - If deployment fails, check code - If code fails, check schema **Document Everything** - Create detailed verification reports - Record evidence at each layer - Document solutions for future reference - Share knowledge with team ## Example: Verifying Conversation Threading **Phase 1: Investigation** ``` Layer 1 (Database): ✅ wade_conversations table exists ✅ wade_messages table exists ✅ All columns present Layer 2 (Deployment): ✅ wade-brain v47 deployed ✅ conversation.ts included ⚠️ JWT verification enabled (should be disabled) Layer 3 (Runtime): ❌ Messages not being stored ❌ last_extraction_at not updating Layer 4 (End-to-End): ❌ Conversation history not working ``` **Phase 2: Pattern Analysis** - Expected: Messages stored after each exchange - Actual: No messages in database - Pattern: JWT verification blocking requests **Phase 3: Hypothesis Testing** ``` Hypothesis: JWT verification is rejecting service role key Test: Disable JWT and retry Evidence: After disabling, requests succeed Conclusion: ✅ CONFIRMED ``` **Phase 4: Implementation** - Disabled JWT verification via dashboard - Redeployed function - Retested: ✅ Messages now storing correctly - Documented: JWT must be disabled for internal services ## Supporting Resources - `references/verification-checklist.md` - Layer-by-layer verification checklists - `references/common-deployment-issues.md` - Known issues and solutions - `templates/verification_report_template.md` - Standard report format ## Related Skills - **systematic-debugging** - When verification reveals issues or bugs, use this skill to identify root causes and implement fixes ## Tips **For Supabase Edge Functions:** - Always verify JWT settings after deployment - Check environment variables are set - Include all files in deployment payload - Test with actual API calls, not just code review **For Database Changes:** - Verify migrations applied successfully - Check RLS policies don't block operations - Test with service role key - Query directly to confirm data structure **For Async Operations:** - Add logging to track completion - Update state before async operations - Use await for critical operations - Handle errors explicitly **For Silent Failures:** - Add descriptive logging with prefixes - Update status before/after operations - Re-throw errors when appropriate - Check logs even when "no errors" reported
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