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Confidence Check — Claude Code skill: score before you code

This skill scores how ready you are before implementation starts. Five checks, and anything under 0.90 stops the agent.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add SuperClaude-Org/SuperClaude_Framework --skill confidence-check -a claude-code
Open on GitHub

What Confidence Check does

The skill produces a confidence score between 0.0 and 1.0 from five weighted checks. Searching the codebase for duplicates is worth 25%, architecture compliance another 25%, official documentation review 20%, a working open source example 15%, and a properly identified root cause 15%.

The total decides what happens next. At 0.90 or above the agent proceeds with implementation. Between 0.70 and 0.90 it presents alternatives and asks questions. Below 0.70 it stops and requests more context. A TypeScript implementation ships in confidence.ts, with the confidenceCheck(context) function, the individual checks and the context interface.

The author's own numbers: spend 100–200 tokens on the check to avoid burning 5000–50000 tokens on work pointed the wrong way. Several checks lean on Context7 MCP, Tavily MCP, WebFetch, WebSearch, Grep and Glob. If those aren't wired up in your setup, the documentation and OSS steps fall back to manual work, so it's worth checking your MCP list first.

What it helps with

  • catch a duplicate function before a second version gets written
  • verify an approach against official docs instead of assumptions
  • find a working open source implementation instead of building your own
  • separate a root cause from a symptom
  • stop the agent when the task lacks enough context

Example prompts for the agent

  • Run a confidence check before implementing this feature and show the five scores
  • Confidence check for the auth task, threshold 0.90
  • Before writing code, search for duplicates and confirm architecture compliance

How to install Confidence Check

1Skills CLI (Vercel)

npx skills add SuperClaude-Org/SuperClaude_Framework --skill confidence-check -a claude-code

2Manually: put the files in place

~/.claude/skills/confidence-check/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/confidence-check/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about Confidence Check

How do I install Confidence Check in Claude?

Run npx skills add SuperClaude-Org/SuperClaude_Framework --skill confidence-check -a claude-code. The skill lands in ~/.claude/skills/confidence-check/SKILL.md.

What does Confidence Check do?

It scores confidence from 0.0 to 1.0 across five checks: duplicates, architecture, official documentation, open source references and root cause. At 0.90 or above the agent moves into implementation.

What does the skill need to work?

Some checks rely on Context7 MCP, Tavily MCP, WebFetch and WebSearch. Without them, documentation review and OSS search become manual steps.

Source text

SKILL.md · MIT
---
name: Confidence Check
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.
---

# 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

```bash
# 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.

© SuperClaude-Org · MIT · SuperClaude-Org/SuperClaude_Framework

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