Confidence Check

GitHub

实施前置信度评估技能,要求≥90%方可执行。通过查重、架构合规、官方文档验证、OSS参考及根因分析五项检查计算分数,旨在避免错误方向工作,节省Token并提升成功率。

skills/confidence-check/SKILL.md SuperClaude-Org/SuperClaude_Framework

Trigger Scenarios

准备开始任何代码实现任务前 需要验证技术栈兼容性和现有功能重复性时

Install

npx skills add SuperClaude-Org/SuperClaude_Framework --skill Confidence Check -g -y
More Options

Use without installing

npx skills use SuperClaude-Org/SuperClaude_Framework@Confidence Check

指定 Agent (Claude Code)

npx skills add SuperClaude-Org/SuperClaude_Framework --skill Confidence Check -a claude-code -g -y

安装 repo 全部 skill

npx skills add SuperClaude-Org/SuperClaude_Framework --all -g -y

预览 repo 内 skill

npx skills add SuperClaude-Org/SuperClaude_Framework --list

SKILL.md

Frontmatter
{
    "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

# 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.

Version History

  • 10be750 Current 2026-07-24 11:55

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Metadata

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Version
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Indexed
2026-07-24 11:55

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