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kinnerkarmanish
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autoresearch

Automate iterative code improvement through measured experiments and git-based logging.

Updated Apr 26, 2026
Search Tags:#automation#git#github#metrics#iterative#experiments#code-improvement
One-click install
npx skills add https://github.com/kinnerkarmanish/mak --skill autoresearch-kinnerkarmanish
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/kinnerkarmanish/mak/tree/main/library/skills/development/autoresearch
Command: npx skills add https://github.com/kinnerkarmanish/mak --skill autoresearch-kinnerkarmanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autonomous loop that continuously improves code by running experiments, measuring outcomes, and deciding to keep or revert changes, reducing manual trial-and-error work.

Core Features & Use Cases

  • Autonomous experimentation cycle: analyze baseline metrics, hypothesize changes, implement, measure, and commit results.
  • Git-backed experiment log: every iteration is recorded with hypothesis, before/after scores, and decisions for traceability.
  • Applicable to performance, reliability, and quality improvements across software projects with measurable targets.

Quick Start

Start the autonomous improvement loop by invoking autoresearch with a metric and a target.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I automate iterative code improvements with measured experiments?▼

Automated iterative code improvement runs an autonomous loop that analyzes baseline metrics, hypothesizes changes, implements them, measures outcomes, and commits or reverts results to reduce manual trial-and-error work.

How does git-based experiment logging work for autonomous code changes?▼

Git-backed experiment logging records every autonomous iteration with the hypothesis, before and after metric scores, and keep or revert decisions, providing full traceability for code improvement progress.

Can I use autonomous experimentation for performance and reliability targets?▼

Autonomous experimentation applies to software projects needing measurable performance, reliability, and quality refinements across baselines, commits, and metrics for targeted code improvements.

What is the best way to track commit-based progress for code quality metrics?▼

Commit-based progress tracking measures code quality by running scripted experiments across git commits, evaluating before and after scores to decide whether to keep or revert changes autonomously.

Do I need predefined metrics to start an autonomous code improvement loop?▼

Start the autonomous improvement loop by invoking the process with a defined metric and a target, which establishes the baseline for hypothesizing, measuring, and committing iterative code changes.

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