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ck:loop

Automate iterative optimization loops for measurable code metrics in git-tracked codebases.

Updated Aug 23, 2026
Search Tags:#automation#git#optimization#metrics#code-quality#loop
One-click install
npx skills add https://github.com/tarang-tj/syllabus-ai --skill ck-loop-tarang-tj
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:loop
Source: https://github.com/tarang-tj/syllabus-ai/tree/main/.claude/skills/ck-loop
Command: npx skills add https://github.com/tarang-tj/syllabus-ai --skill ck-loop-tarang-tj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates autonomous, multi-iteration optimization by proposing and evaluating single atomic changes to improve a measurable metric, with git history preserved for rollback.

Core Features & Use Cases

  • One-atom iteration per cycle with commit-before-verify, guard checks, and result logging
  • Supports configurable iterations, guard commands, and metrics verification
  • Useful for improving code quality metrics (test coverage, bundle size, lint errors) in a reproducible, auditable manner

Quick Start

Initiate the loop with a single atomic change and let it run through the configured iterations.

Frequently Asked Questions about ck:loop

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

FAQPage Schema
How do I automate iterative optimization for code metrics like test coverage or bundle size?▼

Automating iterative optimization requires proposing atomic changes, committing them to git, and verifying the metric before proceeding. This Skill runs that exact loop, logging results to loop-results.tsv for reproducible metric improvements.

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

An autonomous optimization loop applies single atomic changes to a git-tracked codebase, commits before verifying the metric, and uses guard commands to ensure quality. It records iteration outcomes in loop-results.tsv, preserving git history for safe rollback.

Can I use this optimization loop to reduce lint errors in my codebase?▼

Yes, you can use this loop to reduce lint errors. It supports any numeric code metric that can be verifiably measured and guarded across iterations, including lint counts, test coverage, and bundle size.

Does the iterative code optimization loop require a specific environment or dependencies?▼

The loop requires a git-tracked codebase where a numeric metric is verifiably measured. It has no external dependencies, relying on git for atomic changes, guard commands for verification, and loop-results.tsv for logging.

Why should I use an autonomous loop instead of manually refactoring code for metric improvements?▼

Using an autonomous loop enforces atomic changes per iteration with commit-before-verify behavior, ensuring reproducible and auditable improvements. Unlike manual refactoring, it automatically rolls back failed iterations and logs results to loop-results.tsv.

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