What is the Confidence Check skill?

It is a specialized tool for Claude Code that runs a 5-point assessment to ensure an implementation plan is valid, unique, and architecturally sound before any code is written.

What does the skill check for specifically?

It assesses five criteria: absence of duplicates (25%), architecture compliance (25%), official documentation verification (20%), working OSS references (15%), and root cause identification (15%).

How does this skill save tokens?

By spending a small amount of tokens on a pre-check (100-200), you avoid the massive token cost (5,000-50,000) of generating and then fixing an incorrect or redundant implementation.

Why is a 90% confidence score required?

The 90% threshold ensures that the AI has verified the root cause, checked for duplicates, and aligned with project docs, minimizing the risk of 'hallucinated' solutions or technical debt.

Confidence Check

Name: Confidence Check
Author: Activer007

byActiver007

Deployment & DevOps

Validates implementation readiness through a multi-point assessment to prevent wasted effort and architectural drift.

About

The Confidence Check skill acts as a pre-implementation gatekeeper for AI-driven development, ensuring that tasks are executed with high precision and minimal waste. It mandates a 90% confidence threshold before any coding begins, achieved by systematically verifying the absence of duplicate code, confirming alignment with the project's specific architecture, and cross-referencing official documentation. By identifying the root cause and finding proven OSS implementations first, this skill significantly reduces token consumption and prevents the AI from heading in the wrong technical direction.

Key Features

Quantitative scoring system (0.0 - 1.0) with a 90% pass threshold
Architectural alignment checks against CLAUDE.md and PLANNING.md
Automated duplicate detection using Grep and Glob patterns
Root cause identification to ensure fixes target the source of issues
0 GitHub stars
Real-time official documentation and OSS reference verification

Use Cases

Scanning large repositories to prevent the creation of redundant utility functions or modules.
Ensuring a proposed feature implementation aligns with the existing tech stack and design patterns.
Verifying a bug fix strategy before writing code to ensure the root cause is properly identified.

About

Key Features

Quantitative scoring system (0.0 - 1.0) with a 90% pass threshold
Architectural alignment checks against CLAUDE.md and PLANNING.md
Automated duplicate detection using Grep and Glob patterns
Root cause identification to ensure fixes target the source of issues
0 GitHub stars
Real-time official documentation and OSS reference verification

Use Cases

Scanning large repositories to prevent the creation of redundant utility functions or modules.
Ensuring a proposed feature implementation aligns with the existing tech stack and design patterns.
Verifying a bug fix strategy before writing code to ensure the root cause is properly identified.