How does this skill help save on token costs?

By investing 100-200 tokens in a confidence check, you can avoid spending 5,000-50,000 tokens on incorrect, redundant, or hallucinated code implementations.

What criteria are used to determine the confidence score?

It evaluates five areas: no duplicate implementation (25%), architecture compliance (25%), official documentation verification (20%), OSS reference (15%), and root cause identification (15%).

What happens if the confidence score is below 70%?

The framework recommends stopping implementation immediately and requesting additional context or clarification to prevent errors.

What is the primary purpose of the Confidence Check skill?

The primary purpose is to evaluate the reliability of a proposed implementation before code is written, ensuring the AI has enough context and the correct direction.

Confidence Check

Name: Confidence Check
Author: wishdevs

bywishdevs

생산성 및 워크플로

Evaluates task implementation reliability through a weighted scoring framework to prevent incorrect code paths and optimize token usage.

소개

Confidence Check is a strategic assessment framework for Claude Code designed to ensure high-precision development by validating implementation plans before coding begins. By scoring tasks against five critical criteria—including architectural compliance, duplicate detection, and root cause identification—it enforces a strict 90% confidence threshold. This proactive approach prevents wasted effort on redundant or misaligned features, offering an impressive ROI by spending a few hundred tokens to save tens of thousands in potential rework.

주요 기능

Weighted 5-point evaluation system for objective reliability scoring
Architecture alignment verification for specific tech stacks like Supabase and pytest
Token-efficient decision framework with clear Go/Clarify/Stop recommendations
Automated duplicate implementation detection using grep and glob tools
Integration with MCP tools for real-time documentation and OSS reference checking
0 GitHub stars

사용 사례

Checking if a new feature request overlaps with existing functionality to avoid technical debt
Validating a bug fix approach before modifying the codebase to ensure the root cause is addressed
Ensuring complex refactoring tasks align with established project architecture and documentation

소개

주요 기능

Weighted 5-point evaluation system for objective reliability scoring
Architecture alignment verification for specific tech stacks like Supabase and pytest
Token-efficient decision framework with clear Go/Clarify/Stop recommendations
Automated duplicate implementation detection using grep and glob tools
Integration with MCP tools for real-time documentation and OSS reference checking
0 GitHub stars

사용 사례

Checking if a new feature request overlaps with existing functionality to avoid technical debt
Validating a bug fix approach before modifying the codebase to ensure the root cause is addressed
Ensuring complex refactoring tasks align with established project architecture and documentation