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Confidence Check

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.

SKILL.md

Full skill instructions

Confidence Check Skill

Purpose

Prevents wrong-direction execution by assessing confidence BEFORE starting implementation.

Requirement: ≥90% confidence to proceed with implementation.

Test Results (2025-10-21):

  • Precision: 1.000 (no false positives)
  • Recall: 1.000 (no false negatives)
  • 8/​8 test cases passed

When to Use

Use this skill BEFORE implementing any task to ensure:

  • No duplicate implementations exist
  • Architecture compliance verified
  • Official documentation reviewed
  • Working OSS implementations found
  • Root cause properly identified

Confidence Assessment Criteria

Calculate confidence score (0.0 - 1.0) based on 5 checks:

1. No Duplicate Implementations? (25%)

Check: Search codebase for existing functionality

# Use Grep to search for similar functions
# Use Glob to find related modules

✅ Pass if no duplicates found ❌ Fail if similar implementation exists

2. Architecture Compliance? (25%)

Check: Verify tech stack alignment

  • Read CLAUDE.md, PLANNING.md
  • Confirm existing patterns used
  • Avoid reinventing existing solutions

✅ Pass if uses existing tech stack (e.g., Supabase, UV, pytest) ❌ Fail if introduces new dependencies unnecessarily

3. Official Documentation Verified? (20%)

Check: Review official docs before implementation

  • Use Context7 MCP for official docs
  • Use WebFetch for documentation URLs
  • Verify API compatibility

✅ Pass if official docs reviewed ❌ Fail if relying on assumptions

4. Working OSS Implementations Referenced? (15%)

Check: Find proven implementations

  • Use Tavily MCP or WebSearch
  • Search GitHub for examples
  • Verify working code samples

✅ Pass if OSS reference found ❌ Fail if no working examples

5. Root Cause Identified? (15%)

Check: Understand the actual problem

  • Analyze error messages
  • Check logs and stack traces
  • Identify underlying issue

✅ Pass if root cause clear ❌ Fail if symptoms unclear

Confidence Score Calculation

Total = Check1 (25%) + Check2 (25%) + Check3 (20%) + Check4 (15%) + Check5 (15%)

If Total >= 0.90:  ✅ Proceed with implementation
If Total >= 0.70:  ⚠️  Present alternatives, ask questions
If Total < 0.70:   ❌ STOP - Request more context

Output Format

📋 Confidence Checks:
   ✅ No duplicate implementations found
   ✅ Uses existing tech stack
   ✅ Official documentation verified
   ✅ Working OSS implementation found
   ✅ Root cause identified

📊 Confidence: 1.00 (100%)
✅ High confidence - Proceeding to implementation

Implementation Details

The TypeScript implementation is available in confidence.ts for reference, containing:

  • confidenceCheck(context) - Main assessment function
  • Detailed check implementations
  • Context interface definitions

ROI

Token Savings: Spend 100-200 tokens on confidence check to save 5,000-50,000 tokens on wrong-direction work.

Success Rate: 100% precision and recall in production testing.

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