
Confidence Check
- 43 installs
- 51 repo stars
- Updated November 25, 2025
- ovachiever/droid-tings
Helps with ai & agent building tasks during AI-assisted development.
About
confidence check is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- confidence check
- AI & Agent Building
- AI-coding skill
Confidence Check by the numbers
- 43 all-time installs (skills.sh)
- Ranked #7,884 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 27, 2026 (Skillselion catalog sync)
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| Installs | 43 |
|---|---|
| repo stars | ★ 51 |
| Last updated | November 25, 2025 |
| Repository | ovachiever/droid-tings ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
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 contextOutput 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 implementationImplementation 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.
/**
* Confidence Check - Pre-implementation confidence assessment
*
* Prevents wrong-direction execution by assessing confidence BEFORE starting.
* Requires ≥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
*/
export interface Context {
task?: string;
duplicate_check_complete?: boolean;
architecture_check_complete?: boolean;
official_docs_verified?: boolean;
oss_reference_complete?: boolean;
root_cause_identified?: boolean;
confidence_checks?: string[];
[key: string]: any;
}
/**
* Assess confidence level (0.0 - 1.0)
*
* Investigation Phase Checks:
* 1. No duplicate implementations? (25%)
* 2. Architecture compliance? (25%)
* 3. Official documentation verified? (20%)
* 4. Working OSS implementations referenced? (15%)
* 5. Root cause identified? (15%)
*
* @param context - Task context with investigation flags
* @returns Confidence score (0.0 = no confidence, 1.0 = absolute certainty)
*/
export async function confidenceCheck(context: Context): Promise<number> {
let score = 0.0;
const checks: string[] = [];
// Check 1: No duplicate implementations (25%)
if (noDuplicates(context)) {
score += 0.25;
checks.push("✅ No duplicate implementations found");
} else {
checks.push("❌ Check for existing implementations first");
}
// Check 2: Architecture compliance (25%)
if (architectureCompliant(context)) {
score += 0.25;
checks.push("✅ Uses existing tech stack (e.g., Supabase)");
} else {
checks.push("❌ Verify architecture compliance (avoid reinventing)");
}
// Check 3: Official documentation verified (20%)
if (hasOfficialDocs(context)) {
score += 0.2;
checks.push("✅ Official documentation verified");
} else {
checks.push("❌ Read official docs first");
}
// Check 4: Working OSS implementations referenced (15%)
if (hasOssReference(context)) {
score += 0.15;
checks.push("✅ Working OSS implementation found");
} else {
checks.push("❌ Search for OSS implementations");
}
// Check 5: Root cause identified (15%)
if (rootCauseIdentified(context)) {
score += 0.15;
checks.push("✅ Root cause identified");
} else {
checks.push("❌ Continue investigation to identify root cause");
}
// Store check results
context.confidence_checks = checks;
// Display checks
console.log("📋 Confidence Checks:");
checks.forEach(check => console.log(` ${check}`));
console.log("");
return score;
}
/**
* Check for duplicate implementations
*
* Before implementing, verify:
* - No existing similar functions/modules (Glob/Grep)
* - No helper functions that solve the same problem
* - No libraries that provide this functionality
*/
function noDuplicates(context: Context): boolean {
return context.duplicate_check_complete ?? false;
}
/**
* Check architecture compliance
*
* Verify solution uses existing tech stack:
* - Supabase project → Use Supabase APIs (not custom API)
* - Next.js project → Use Next.js patterns (not custom routing)
* - Turborepo → Use workspace patterns (not manual scripts)
*/
function architectureCompliant(context: Context): boolean {
return context.architecture_check_complete ?? false;
}
/**
* Check if official documentation verified
*
* For testing: uses context flag 'official_docs_verified'
* For production: checks for README.md, CLAUDE.md, docs/ directory
*/
function hasOfficialDocs(context: Context): boolean {
// Check context flag (for testing and runtime)
if ('official_docs_verified' in context) {
return context.official_docs_verified ?? false;
}
// Fallback: check for documentation files (production)
// This would require filesystem access in Node.js
return false;
}
/**
* Check if working OSS implementations referenced
*
* Search for:
* - Similar open-source solutions
* - Reference implementations in popular projects
* - Community best practices
*/
function hasOssReference(context: Context): boolean {
return context.oss_reference_complete ?? false;
}
/**
* Check if root cause is identified with high certainty
*
* Verify:
* - Problem source pinpointed (not guessing)
* - Solution addresses root cause (not symptoms)
* - Fix verified against official docs/OSS patterns
*/
function rootCauseIdentified(context: Context): boolean {
return context.root_cause_identified ?? false;
}
/**
* Get recommended action based on confidence level
*
* @param confidence - Confidence score (0.0 - 1.0)
* @returns Recommended action
*/
export function getRecommendation(confidence: number): string {
if (confidence >= 0.9) {
return "✅ High confidence (≥90%) - Proceed with implementation";
} else if (confidence >= 0.7) {
return "⚠️ Medium confidence (70-89%) - Continue investigation, DO NOT implement yet";
} else {
return "❌ Low confidence (<70%) - STOP and continue investigation loop";
}
}