125 lines
3.2 KiB
Markdown
125 lines
3.2 KiB
Markdown
---
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name: Confidence Check
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description: Pre-implementation confidence assessment (≥90% required). Use before starting any implementation to verify readiness with duplicate check, architecture compliance, official docs verification, OSS references, and root cause identification.
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---
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# Confidence Check Skill
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## Purpose
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Prevents wrong-direction execution by assessing confidence **BEFORE** starting implementation.
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**Requirement**: ≥90% confidence to proceed with implementation.
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**Test Results** (2025-10-21):
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- Precision: 1.000 (no false positives)
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- Recall: 1.000 (no false negatives)
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- 8/8 test cases passed
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## When to Use
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Use this skill BEFORE implementing any task to ensure:
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- No duplicate implementations exist
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- Architecture compliance verified
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- Official documentation reviewed
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- Working OSS implementations found
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- Root cause properly identified
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## Confidence Assessment Criteria
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Calculate confidence score (0.0 - 1.0) based on 5 checks:
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### 1. No Duplicate Implementations? (25%)
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**Check**: Search codebase for existing functionality
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```bash
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# Use Grep to search for similar functions
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# Use Glob to find related modules
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```
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✅ Pass if no duplicates found
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❌ Fail if similar implementation exists
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### 2. Architecture Compliance? (25%)
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**Check**: Verify tech stack alignment
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- Read `CLAUDE.md`, `PLANNING.md`
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- Confirm existing patterns used
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- Avoid reinventing existing solutions
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✅ Pass if uses existing tech stack (e.g., Supabase, UV, pytest)
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❌ Fail if introduces new dependencies unnecessarily
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### 3. Official Documentation Verified? (20%)
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**Check**: Review official docs before implementation
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- Use Context7 MCP for official docs
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- Use WebFetch for documentation URLs
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- Verify API compatibility
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✅ Pass if official docs reviewed
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❌ Fail if relying on assumptions
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### 4. Working OSS Implementations Referenced? (15%)
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**Check**: Find proven implementations
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- Use Tavily MCP or WebSearch
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- Search GitHub for examples
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- Verify working code samples
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✅ Pass if OSS reference found
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❌ Fail if no working examples
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### 5. Root Cause Identified? (15%)
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**Check**: Understand the actual problem
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- Analyze error messages
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- Check logs and stack traces
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- Identify underlying issue
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✅ Pass if root cause clear
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❌ Fail if symptoms unclear
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## Confidence Score Calculation
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```
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Total = Check1 (25%) + Check2 (25%) + Check3 (20%) + Check4 (15%) + Check5 (15%)
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If Total >= 0.90: ✅ Proceed with implementation
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If Total >= 0.70: ⚠️ Present alternatives, ask questions
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If Total < 0.70: ❌ STOP - Request more context
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```
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## Output Format
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```
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📋 Confidence Checks:
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✅ No duplicate implementations found
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✅ Uses existing tech stack
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✅ Official documentation verified
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✅ Working OSS implementation found
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✅ Root cause identified
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📊 Confidence: 1.00 (100%)
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✅ High confidence - Proceeding to implementation
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```
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## Implementation Details
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The TypeScript implementation is available in `confidence.ts` for reference, containing:
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- `confidenceCheck(context)` - Main assessment function
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- Detailed check implementations
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- Context interface definitions
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## ROI
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**Token Savings**: Spend 100-200 tokens on confidence check to save 5,000-50,000 tokens on wrong-direction work.
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**Success Rate**: 100% precision and recall in production testing.
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