Does it work with any programming language?

Yes, it uses universal search patterns and documentation lookups that are language-agnostic, making it suitable for any software project.

Why does this skill require a 90% confidence score?

The 90% threshold ensures maximum precision, preventing the AI from proceeding with 'hallucinated' solutions or redundant implementations that could introduce technical debt.

What happens if the confidence score is low?

If the score is below 70%, the skill instructs Claude to stop and request more context or clarification from the user rather than guessing.

How does Confidence Check save on token usage?

By spending 100-200 tokens on a preliminary check, you avoid spending 5,000+ tokens on an incorrect implementation that would eventually need to be reverted or fixed.

Confidence Check

Name: Confidence Check
Author: swcstudiospace

byswcstudiospace

生産性とワークフロー

Validates implementation readiness through a rigorous 90% confidence threshold to prevent architectural drift and redundant code.

概要

Confidence Check is a strategic pre-implementation safeguard designed for Claude Code to ensure that every development task is well-founded before execution begins. By evaluating tasks against five critical criteria—duplicate detection, architectural compliance, official documentation verification, open-source references, and root cause identification—it enforces a strict 90% confidence score requirement. This proactive approach saves significant token costs and developer time by preventing 'wrong-direction' work and ensuring that all changes align perfectly with existing project patterns and verified technical standards.

主な機能

Automated duplicate implementation search to prevent redundant code
0 GitHub stars
Architectural alignment verification against project standards like CLAUDE.md
Root cause analysis to ensure fixes address the core problem, not just symptoms
Quantitative readiness scoring (0-1.0) with a 90% success threshold
Real-time documentation and OSS reference validation using MCP tools

ユースケース

Preventing redundant utility functions in large, multi-contributor codebases
Ensuring new features adhere to specific tech stacks like Supabase or UV
Verifying complex bug fixes before modifying core library logic

概要

主な機能

Automated duplicate implementation search to prevent redundant code
0 GitHub stars
Architectural alignment verification against project standards like CLAUDE.md
Root cause analysis to ensure fixes address the core problem, not just symptoms
Quantitative readiness scoring (0-1.0) with a 90% success threshold
Real-time documentation and OSS reference validation using MCP tools

ユースケース

Preventing redundant utility functions in large, multi-contributor codebases
Ensuring new features adhere to specific tech stacks like Supabase or UV
Verifying complex bug fixes before modifying core library logic