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Confidence Check

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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.

Source

git clone https://github.com/Microck/ordinary-claude-skills/blob/main/skills_all/confidence-check/SKILL.mdView on GitHub

Overview

Confidence Check is a pre-implementation assessment that must reach ≥90% before any work begins. It verifies duplicates, architecture compliance, official docs verification, OSS references, and root-cause identification to prevent wrong-direction efforts.

How This Skill Works

The skill runs five checks (duplicate search, architecture alignment, docs verification, OSS references, and root-cause clarity) to compute a confidence score from 0.0 to 1.0. If the total score is ≥0.9, you proceed; lower scores trigger warnings or request more context.

When to Use It

  • Before starting any new implementation task to ensure readiness.
  • When there might be duplicate functionality in the codebase.
  • When validating architecture compliance and existing tech patterns.
  • When official documentation and API compatibility must be verified.
  • When root-cause analysis and actionable debugging paths are needed.

Quick Start

  1. Step 1: Use Grep and Glob to scan for existing implementations.
  2. Step 2: Verify architecture alignment against CLAUDE.md and PLANNING.md.
  3. Step 3: Review official docs via WebFetch and locate OSS examples; assess root cause.

Best Practices

  • Search the codebase with Grep for similar functions before coding.
  • Cross-check CLAUDE.md and PLANNING.md for alignment.
  • Review official docs with WebFetch and corroborate API compatibility.
  • Look for working OSS implementations as references with WebSearch.
  • Document the root cause findings and keep the confidence score visible.

Example Use Cases

  • A new feature module with no duplicates found in the repository.
  • Refactoring a component only after confirming architecture compatibility.
  • Official docs reviewed and no breaking changes detected.
  • An OSS reference demonstrating a working approach before implementation.
  • Root-cause analysis completed for an observed bug prior to coding a fix.

Frequently Asked Questions

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