ck:loop

Run iterative experiments to optimize measurable metrics in git repositories.

1|Updated Mar 5, 2026
One-click install
npx skills add https://github.com/hotriluan/alkana_web --skill ck-loop
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ck:loop
Source: https://github.com/hotriluan/alkana_web/tree/main/.opencode/skills/ck-loop
Command: npx skills add https://github.com/hotriluan/alkana_web --skill ck-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Autonomous iterative optimization loop for improving measurable metrics by running N iterations, learning from git history, and automatically keeping or discarding changes.

Core Features & Use Cases

  • Autonomous iterations to optimize metrics such as coverage, performance, and bundle size
  • Learns from historical commits and applies strict, atomic changes with rollback on regression
  • Use cases include automated experimentation in CI or local git repos with automatic metric-driven improvements

Quick Start

Invoke ck:loop with a defined Goal, Scope, and Verify to begin iterative optimization.

Frequently Asked Questions about ck:loop

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

FAQPage Schema
How do I automate metric-driven optimization for my software project?

Automated optimization uses iterative experiments to apply atomic changes against a defined metric, keeping improvements and automatically rolling back regressions by learning from git history.

What is an autonomous optimization loop and how does it work with git history?

An autonomous optimization loop runs iterative experiments against a mechanical metric, learning from historical commits to automatically keep or discard atomic changes based on regression results.

How do I set up autonomous iterations to improve test coverage or bundle size?

Set up autonomous iterations by providing a defined Goal, a Scope glob, and a Verify command that returns a single numeric value, using optional Guard and Iterations settings to control execution.

Do I need a clean git repository to run automated experimentation loops?

Yes, automated experimentation requires a git repository with a clean working tree to safely apply atomic changes, evaluate the mechanical metric, and roll back regressions without conflicts.

Can I use autonomous optimization loops for performance improvements in CI?

Yes, autonomous optimization supports automated experimentation in CI pipelines or local git repos to iteratively improve measurable targets like performance, coverage, and bundle size.

What happens to code changes if an iteration causes a metric regression?

If an iteration causes a metric regression, the autonomous loop automatically discards the atomic changes and rolls back the commit, ensuring only measurable improvements are kept in the repository.