cainish
NewSelf-improvement system for Claude Code skills. Analyzes sessions for improvement signals, proposes skill enhancements, validates via critic agent, and tracks effectiveness metrics.
Summary
cainish is a self-improvement system for Claude Code skills that analyzes sessions for improvement signals, proposes skill enhancements, validates them via a critic agent, and tracks effectiveness metrics.
- It helps developers continuously refine their AI-assisted workflows by automatically identifying bottlenecks and suggesting targeted skill updates.
Install & Usage
mkdir -p .claude/skillsmkdir -p .claude/skills && curl -o .claude/skills/cainish.md https://raw.githubusercontent.com/Cain-Ish/claude-skills/main/SKILL.md/cainishUse Cases
Usage Examples
/cainish review last session
/cainish propose enhancement for skill 'deploy' based on recent errors
/cainish track effectiveness of skill 'refactor' over the past week
Security Audits
Frequently Asked Questions
What is cainish?
cainish is a self-improvement system for Claude Code skills that analyzes sessions for improvement signals, proposes skill enhancements, validates them via a critic agent, and tracks effectiveness metrics. It helps developers continuously refine their AI-assisted workflows by automatically identifying bottlenecks and suggesting targeted skill updates.
How to install cainish?
To install cainish: create the skills directory (mkdir -p .claude/skills), then run: mkdir -p .claude/skills && curl -o .claude/skills/cainish.md https://raw.githubusercontent.com/Cain-Ish/claude-skills/main/SKILL.md. Finally, /cainish in Claude Code.
What is cainish best for?
cainish is a skill categorized under Development. It is designed for: agent, self-improvement, learning, skills, metrics, reflection. Created by Cain-Ish.
What can I use cainish for?
cainish is useful for: Automatically review a completed coding session to identify where the AI's responses could be more efficient or accurate.; Propose and validate a new skill enhancement to reduce repetitive manual steps in a deployment workflow.; Track the effectiveness of a skill update over multiple sessions to measure improvement in task completion time.; Identify common error patterns in AI-generated code and suggest skill modifications to prevent them.; Generate a report of skill usage metrics to prioritize which skills need refinement.; Validate a proposed skill change against a set of test scenarios before applying it to production skills..