What is the Confidence Check skill?

It is a specialized diagnostic tool for Claude Code that runs a series of checks to ensure a task is fully understood and technically viable before any code is actually written.

How does it verify architecture compliance?

The skill reviews project-specific files like CLAUDE.md or PLANNING.md and searches the codebase to ensure new implementations align with the existing tech stack and design patterns.

Does it check for existing code?

Yes, it uses grep and glob searches to scan your repository for duplicate or similar functionality to prevent 'reinventing the wheel'.

Can this skill help reduce AI costs?

Yes. By spending a small amount of tokens (100-200) on a pre-check, you can avoid wasting thousands of tokens on an implementation that would have failed due to incorrect assumptions.

Why is the 90% confidence threshold important?

The threshold ensures that Claude has verified all necessary documentation, architectural constraints, and root causes, preventing 'hallucinations' or incorrect implementations that lead to technical debt.

Confidence Check

Name: Confidence Check
Author: swcstudio

byswcstudio

Productivity & Workflow

Performs pre-implementation assessments to ensure technical readiness and prevent redundant or incorrect code execution.

About

The Confidence Check skill serves as a rigorous pre-flight diagnostic tool for AI-driven development, requiring a 90% confidence threshold before any implementation begins. By systematically evaluating codebase duplicates, architectural alignment, official documentation, open-source references, and root cause identification, it prevents 'wrong-direction' work. This skill is specifically designed to optimize token usage and developer efficiency by ensuring every coding task is grounded in verified context and proven patterns before a single line of code is written.

Key Features

Automated duplicate code detection to prevent redundant implementations
Architectural compliance verification against project standards and tech stacks
Root cause analysis to ensure the underlying issue is properly identified
Official documentation validation via web search and API reference checks
0 GitHub stars
Open-source reference lookup to find proven implementation patterns

Use Cases

Reducing token waste by identifying missing context before initiating high-cost tasks
Ensuring new code adheres to strict architectural guidelines like Supabase or specific testing patterns
Preventing redundant feature implementation in complex or unfamiliar codebases

About

Key Features

Automated duplicate code detection to prevent redundant implementations
Architectural compliance verification against project standards and tech stacks
Root cause analysis to ensure the underlying issue is properly identified
Official documentation validation via web search and API reference checks
0 GitHub stars
Open-source reference lookup to find proven implementation patterns

Use Cases

Reducing token waste by identifying missing context before initiating high-cost tasks
Ensuring new code adheres to strict architectural guidelines like Supabase or specific testing patterns
Preventing redundant feature implementation in complex or unfamiliar codebases