confidence-check

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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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[Guardrails and Safety]

When & Why to Use This Skill

The Confidence Check skill is a sophisticated pre-implementation assessment tool designed to ensure AI agents operate with high precision. By enforcing a strict 90% confidence threshold, it evaluates implementation readiness through five critical dimensions: duplicate code detection, architecture compliance, official documentation verification, OSS reference checks, and root cause identification. This skill acts as a vital guardrail, preventing 'wrong-direction' execution and significantly optimizing token usage by halting low-confidence tasks before they consume resources.

Use Cases

  • Redundancy Prevention: Automatically scans the existing codebase to ensure the agent does not recreate existing functions or modules, maintaining project DRY (Don't Repeat Yourself) standards.
  • Architectural Alignment: Verifies that proposed implementations strictly follow the project's tech stack and design patterns (e.g., Supabase, UV, or specific testing frameworks) as defined in system prompts or planning docs.
  • Technical Validation: Cross-references implementation plans with official documentation and proven Open Source (OSS) examples to ensure API compatibility and avoid reliance on hallucinations.
  • Root Cause Verification: Ensures the underlying problem is fully understood through log analysis and stack trace review before implementation begins, preventing superficial or 'band-aid' fixes.
  • Token & Cost Optimization: Provides a high-ROI mechanism that spends minimal tokens on a confidence check to save thousands of tokens that would otherwise be wasted on incorrect or circular implementation attempts.
nameConfidence Check
descriptionPre-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.

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.