Does it automatically modify my existing skills?

No, it provides structured assessments and suggestions. You retain full control over which skills are actually created or updated based on its recommendations.

What is the primary benefit of using Meta-Skill?

It transforms unique interactions into repeatable, high-quality skills and insights, preventing manual repetition of complex tasks and improving AI accuracy.

How do I trigger the Meta-Skill?

You can trigger it explicitly using the /meta-skill command, or implicitly by using keywords like 'review this conversation', 'optimize skills', or 'can we reuse this process?'

What happens during a 'review-all' session?

The skill runs an analysis script across your interaction history to identify which skills are being used effectively, which are redundant, and where your skill library has gaps.

Meta-Skill: Retrospective & Evolution

Name: Meta-Skill: Retrospective & Evolution
Author: ttmouse

byttmouse

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Productividad y Flujo de Trabajo

Analyzes conversation history to refine existing skills and identify repeatable workflows for continuous AI improvement.

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The Meta-Skill provides a metacognitive layer to Claude, enabling it to conduct retrospectives on its own performance. By evaluating used skills, identifying high-value workflows for automation, and logging insights into an evolution file, it transforms one-off interactions into a self-improving skill ecosystem. It focuses on extracting transferable insights rather than just summarizing facts, ensuring that the AI becomes more efficient and tailored to the user's specific needs over time.

Características Principales

Automated assessment of existing skill performance and triggers
Conversation retrospectives for deep insight extraction
Non-intrusive logging of evolution data to track system growth
Identification and drafting of new potential skills from complex workflows
System-wide skill health checks and usage analysis via review-all
31 GitHub stars

Casos de Uso

Auditing the efficiency of current skill triggers to reduce friction
Refining complex developer workflows after completing a multi-stage task
Converting successful problem-solving sequences into reusable automation skills

Acerca de

Características Principales

Automated assessment of existing skill performance and triggers
Conversation retrospectives for deep insight extraction
Non-intrusive logging of evolution data to track system growth
Identification and drafting of new potential skills from complex workflows
System-wide skill health checks and usage analysis via review-all
31 GitHub stars

Casos de Uso

Auditing the efficiency of current skill triggers to reduce friction
Refining complex developer workflows after completing a multi-stage task
Converting successful problem-solving sequences into reusable automation skills