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Confidence Check

Confidence Check

github.com/superclaude-orgAgent workflows23.9k316 installsUpdated 4w ago
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About Confidence Check

Confidence Check is an agent skill that evaluates implementation readiness before work begins. It scores five checks and defines when to proceed, investigate alternatives, or request more context.

Key features

  1. 01Five weighted readiness checks
  2. 02Score-based proceed thresholds
  3. 03Checks for duplicates and architecture fit
  4. 04Documentation and OSS reference review
  5. 05Root-cause assessment

Use cases

  1. 01Assess a task before implementation
  2. 02Find missing research or context
  3. 03Avoid duplicate or incompatible solutions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/superclaude-org/superclaude_framework --skill confidence-check

After installing, type/confidence-checkin Claude Code to confirm it loaded.

Bundled filesconfidence.ts

Details

From the project README

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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