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ck:autoresearch

Automate iterative code optimization using git history and a mechanical metric.

Updated Apr 23, 2026
Search Tags:#code metrics#git history#code improvement#autonomous optimization#iterative experimentation
One-click install
npx skills add https://github.com/snake-dev-team/snake-backlink-forge --skill ck-autoresearch-snake-dev-team
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:autoresearch
Source: https://github.com/snake-dev-team/snake-backlink-forge/tree/main/.claude/skills/ck-autoresearch
Command: npx skills add https://github.com/snake-dev-team/snake-backlink-forge --skill ck-autoresearch-snake-dev-team

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The ck:autoresearch Skill addresses the challenge of optimizing code metrics such as coverage, performance, and bundle size, by automating the iterative process of experimentation and learning from git history.

Core Features & Use Cases

  • Autonomous Optimization: Automatically runs N iterations against a mechanical metric, learning and adjusting changes based on git history.
  • Code Metrics Improvement: Use for improving measurable metrics through repeated experimentation.
  • Use Case: If you want to increase test coverage in your codebase, you can use this Skill to automatically iterate on changes and learn which modifications lead to improvements.

Quick Start

Run the ck:autoresearch Skill with the goal of increasing test coverage and specifying the scope of files to be optimized.

Frequently Asked Questions about ck:autoresearch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate test coverage improvement in my codebase?▼

Autonomous code optimization automatically runs iterations against a mechanical metric, learning from git history to adjust changes and improve measurable metrics like coverage, performance, and bundle size.

What do I need to run autonomous code optimization on my repository?▼

Autonomous code optimization requires a git repository with a clean working tree and a specific verify command to measure progress and validate that iterative experimentation leads to metric improvements.

Can I optimize bundle size and performance through iterative experimentation?▼

Yes, autonomous optimization can improve bundle size and performance by running iterative experimentation that measures progress against mechanical metrics and learns from git history.

How does learning from git history help with code improvement?▼

Learning from git history helps code improvement by analyzing previous modifications to determine which changes positively impact mechanical metrics, allowing the autonomous loop to adjust future iterations accordingly.

What are the limitations of using an autonomous optimization loop for code metrics?▼

Limitations of autonomous optimization loops include the requirement for a clean git working tree and a defined verify command, meaning it cannot run with uncommitted changes or without a way to measure progress.

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