Does this skill help save on token costs?

Yes. By investing a small amount of tokens in a pre-check, you can save thousands of tokens that might otherwise be wasted on incorrect code or troubleshooting wrong-direction implementations.

Can I use this with my own project documentation?

Absolutely. The skill is designed to search your codebase and reference files like CLAUDE.md or PLANNING.md to ensure every new implementation respects your specific project constraints.

What happens if the confidence score is low?

If the score is between 70% and 90%, Claude will present alternative approaches or ask clarifying questions. If it falls below 70%, it will stop the task entirely and request additional context.

How does the confidence score prevent errors?

The skill evaluates five critical areas: duplicate checks, architecture alignment, official documentation, OSS references, and root cause analysis. If the combined score is below 90%, implementation is paused to prevent hallucinations.

Confidence Check Guard

Name: Confidence Check Guard
Author: swcstudio

byswcstudio

Productividad y Flujo de Trabajo

Ensures development accuracy by performing a mandatory pre-implementation assessment to prevent hallucinations and redundant code.

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Confidence Check is a strategic safeguard for Claude Code that enforces a rigorous pre-implementation protocol. It verifies implementation readiness by checking for existing codebase duplicates, ensuring architecture compliance, validating against official documentation, and identifying root causes. By requiring a minimum 90% confidence score before proceeding, this skill prevents costly 'wrong-direction' execution, significantly reducing token consumption and ensuring high-quality, maintainable code contributions that align with your existing tech stack.

Características Principales

0 GitHub stars
Mandatory 90% confidence score threshold for implementation
Documentation-first validation using official web resources
Automated duplicate detection to prevent redundant logic
Architectural alignment verification against established patterns
Root cause identification to resolve underlying issues

Casos de Uso

Preventing the creation of duplicate utility functions in large codebases
Ensuring new features strictly align with patterns defined in CLAUDE.md or PLANNING.md
Verifying API compatibility with official docs before writing complex backend logic

Acerca de

Características Principales

0 GitHub stars
Mandatory 90% confidence score threshold for implementation
Documentation-first validation using official web resources
Automated duplicate detection to prevent redundant logic
Architectural alignment verification against established patterns
Root cause identification to resolve underlying issues

Casos de Uso

Preventing the creation of duplicate utility functions in large codebases
Ensuring new features strictly align with patterns defined in CLAUDE.md or PLANNING.md
Verifying API compatibility with official docs before writing complex backend logic