tkm:auto-research

Automate iterative code optimization with machine learning and git rollback.

Updated May 13, 2026

Search Tags:#machine learning #git integration #performance improvement #code optimization #iterative testing

Ready to run

npx skills add https://github.com/khapn-2933/agentic-coding-hands-on --skill tkm-auto-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tkm:auto-research
Source: https://github.com/khapn-2933/agentic-coding-hands-on/tree/main/.claude/skills/auto-research
Command: npx skills add https://github.com/khapn-2933/agentic-coding-hands-on --skill tkm-auto-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, numpy, pandas, gitpython, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the iterative process of code optimization across multiple runs against a measurable metric, learning from git history and allowing automatic rollback of changes to prevent regressions.

Core Features & Use Cases

Automated Code Optimization: Iteratively optimize code for metrics like test coverage, performance, and bundle size.
Machine Learning: Uses machine learning to predict the impact of code changes.
Git Integration: Leverages git history for learning and rollback capabilities.
Use Case: For example, you can use this Skill to automatically increase test coverage in your codebase by making and testing changes iteratively.

Quick Start

Run the tkm:auto-research skill with the desired goal, scope, and verification command. Example: /tkm:auto-research Goal: Increase test coverage Scope: src/**/*.ts Verify: npx jest --coverage --json | jq '.coverageMap | .. | .s? | to_entries | map(.value) | (map(select(.>0)) | length) / length * 100'

Frequently Asked Questions about tkm:auto-research

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

FAQPage Schema

How do I automate code optimization for performance and test coverage?▼

Automate code optimization by iteratively applying changes and measuring metrics like test coverage or performance. This Skill uses machine learning to predict impacts and git history to prevent regressions via automatic rollback.

Can I use machine learning to predict the impact of code changes?▼

Yes, machine learning predicts the impact of code changes. The Skill leverages scikit-learn and numpy to analyze iterative testing results, helping target measurable metrics like bundle size and performance improvements.

How do I integrate git history for iterative testing and rollback?▼

Integrate git history for iterative testing by tracking changes with gitpython. The Skill uses this history to learn from previous runs and automatically rolls back changes if regressions are detected during code optimization.

What do I need to run automated code optimization with machine learning?▼

You need Python, a git repository with a clean working tree, and dependencies like pandas, scikit-learn, and gitpython. You must also define a goal, scope, and verification command to measure the target metric.

When should I avoid using machine learning for code optimization?▼

Avoid using machine learning for code optimization if your repository does not have a clean working tree or lacks measurable verification commands. The Skill requires git history and quantifiable metrics like bundle size to function correctly.