How do I install the Confidence Check skill?

Run npx get-skill add tomevault-io/confidence-check in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Confidence Check free to use?

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Who published Confidence Check?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Confidence Check by tomevault-io — Agent Skill

Name: Confidence Check
Author: tomevault-io

Confidence Check

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. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

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.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: confidence-checkdescription: 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. Use when this capability is needed.---# Confidence Check Skill## PurposePrevents 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 UseUse 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 CriteriaCalculate 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 implementationIf Total >= 0.70: ⚠️ Present alternatives, ask questionsIf 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 DetailsThe 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.---> Converted and distributed by [TomeVault](https://tomevault.io/claim/rengotaku) — claim your Tome and manage your conversions.

Frequently asked questions