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ck:autoresearch

Automate iterative code experiments to improve measurable metrics via git commits.

Updated Apr 2, 2026
Search Tags:#git#optimization#autonomous#test-coverage#bundle-size#guard#verify-command
One-click install
npx skills add https://github.com/hidonguyen/trade-ops --skill ck-autoresearch-hidonguyen
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:autoresearch
Source: https://github.com/hidonguyen/trade-ops/tree/main/.opencode/skills/ck-autoresearch
Command: npx skills add https://github.com/hidonguyen/trade-ops --skill ck-autoresearch-hidonguyen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates iterative, measurable code experiments to improve mechanical metrics (for example test coverage, bundle size, or lint error counts) by making one atomic change per iteration, committing before verification, and keeping or reverting changes based on numeric results and guard checks.

Core Features & Use Cases

  • Autonomous Iterations: Runs a repeated loop of edit → commit → verify → decide over a configurable number of iterations.
  • Guarded Verification: Supports an optional guard command to prevent regressions and enforces a verify command that prints a single numeric metric.
  • Git-as-Memory Workflow: Uses commits and git revert to preserve experiment history and enable pattern analysis across iterations.
  • Use Cases: Increase test coverage in a target folder, reduce main bundle size, or drive ESLint error count toward zero within a scoped set of files.

Quick Start

Start an autonomous loop by providing a Goal, a Scope glob, and a Verify command that prints one numeric value (for example: Goal: Increase test coverage in src/utils; Scope: src/utils/**/*.ts; Verify: npx jest --coverage --coverageReporters=json-summary).

Frequently Asked Questions about ck:autoresearch

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

FAQPage Schema
How do I automate increasing test coverage in a git-tracked codebase?▼

You can automate increasing test coverage by running an autonomous loop that edits code, commits changes, and verifies the new coverage metric, keeping improvements and reverting regressions.

What is an autonomous metric-driven optimization loop for code?▼

An autonomous metric-driven optimization loop is a process that iteratively edits code to improve a measurable metric, using git commits and reverts to track experiment history and discard failed changes.

Can I use a guard command to prevent regressions when reducing bundle size?▼

Yes, you can use an optional guard command alongside the verify command to prevent regressions while reducing bundle size, ensuring changes do not break existing functionality.

Do I need a clean git working tree to run automated code experiments?▼

Yes, you need a clean git working tree to run automated code experiments, because the workflow commits changes before verification and uses git revert to discard failed iterations.

What are the requirements for the verify command in an autonomous optimization loop?▼

The verify command must output a single numeric value within 30 seconds, allowing the loop to objectively measure metrics like lint error count or bundle size and decide whether to keep changes.

How do I scope code changes when driving ESLint error count toward zero?▼

You scope code changes by providing a file glob pattern, ensuring the autonomous loop only edits targeted files to drive ESLint error count toward zero without touching unrelated code.

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