How does the skill determine what qualifies as a 'candidate'?

By default, solutions qualify if they have a success rate of 70% or higher and have been successfully applied at least three times without yet being promoted to a formal skill.

Does identifying a candidate automatically create a new skill?

No, this skill identifies the best candidates. Once identified, you can review the details and use the /matrix:create-skill command to finalize the promotion.

What is the Promotion Score?

The Promotion Score is a 0-100 metric that combines success rate (50%), usage frequency (30%), and a preference for medium complexity (20%) to identify the most practical solutions for reuse.

Can I filter the candidates by specific performance metrics?

Yes, you can use arguments like 'threshold 0.8' to find only the highest-performing solutions or 'top 5' to limit the results to the best candidates.

Matrix Skill Candidates

Name: Matrix Skill Candidates
Author: ojowwalker77

byojowwalker77

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생산성 및 워크플로

Identifies high-performing Matrix solutions suitable for promotion to reusable Claude Code Skills based on performance metrics.

소개

The Matrix Skill Candidates skill streamlines the evolution of your AI development environment by identifying repeatable, high-value coding patterns within the Matrix ecosystem. By analyzing historical success rates, usage frequency, and implementation complexity, it provides a data-driven shortlist of solutions that are ready to be formalized into standalone Claude Code Skills. This allows developers to transition from one-off fixes to a robust library of reusable capabilities, significantly improving long-term productivity and agent reliability.

주요 기능

Automated identification of high-success coding patterns
Weighted promotion scoring based on reliability and complexity
Seamless integration with Matrix skill creation workflows
81 GitHub stars
Customizable filtering for success thresholds and usage counts
Detailed reporting on solution performance and suggested naming

사용 사례

Auditing the reliability and success rates of existing Matrix solutions
Scaling AI productivity by formalizing repeated workflows into reusable skills
Identifying high-value automation targets within a large codebase

소개

주요 기능

Automated identification of high-success coding patterns
Weighted promotion scoring based on reliability and complexity
Seamless integration with Matrix skill creation workflows
81 GitHub stars
Customizable filtering for success thresholds and usage counts
Detailed reporting on solution performance and suggested naming

사용 사례

Auditing the reliability and success rates of existing Matrix solutions
Scaling AI productivity by formalizing repeated workflows into reusable skills
Identifying high-value automation targets within a large codebase