self-improve

Orchestrate multi-agent codebase improvement experiments with tournament selection.

5|Updated Mar 16, 2026

Search Tags:#automation #git #optimization #self-improvement #multi-agent #orchestration #benchmark

Ready to run

npx skills add https://github.com/RobinNorberg/oh-my-copilot --skill self-improve-robinnorberg

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improve
Source: https://github.com/RobinNorberg/oh-my-copilot/tree/main/skills/self-improve
Command: npx skills add https://github.com/RobinNorberg/oh-my-copilot --skill self-improve-robinnorberg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Autonomous self-improvement of a codebase is orchestrated as an end-to-end loop that coordinates multiple agents and a tournament-style selection to identify superior changes.

Core Features & Use Cases

End-to-end automation: from goal clarification and benchmarking to planning, execution, and verification.
Tournament-based evaluation: multiple candidate plans are generated, tested, and the best is merged after validation.
Traceable history and visualization: iteration histories, benchmarks, and progress visuals are stored for analysis.
Guardrails and safety: enforced via harness rules, sealed-files checks, and trust gates to prevent unsafe modifications.

Quick Start

Configure your target repository and goal, then start the self-improvement loop to iteratively optimize your codebase.

Frequently Asked Questions about self-improve

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

FAQPage Schema

How does autonomous code improvement via tournament selection work?▼

Autonomous code improvement orchestrates multi-agent experiments where multiple candidate plans are generated, tested against benchmarks, and the superior change is selected for merging. This tournament-style evaluation ensures only validated modifications improve the codebase.

How do I automate end-to-end optimization of my codebase across multiple repositories?▼

You automate end-to-end codebase optimization by configuring your target repository and goal, then starting a self-improvement loop. The loop handles goal clarification, benchmarking, planning, execution, evaluation, and recording automatically across multiple languages and repos.

Can I use multi-agent orchestration to iteratively optimize software projects with strict guardrails?▼

Yes, multi-agent orchestration applies to software projects needing iterative optimization with strict guardrails. The process enforces safety via harness rules, sealed-files checks, and trust gates to prevent unsafe modifications during the automated improvement loop.

What is the best way to evaluate and merge code changes generated by multiple agents?▼

The best way to evaluate multi-agent code changes is tournament-based selection. Multiple candidate plans undergo benchmarking and validation, and only the highest-performing change is merged after passing strict trust gates and verification.

How are benchmarking results and iteration histories tracked during automated codebase optimization?▼

Benchmarking results, iteration histories, and progress visuals are stored for analysis during automated codebase optimization. This traceable history records each step from goal clarification to merge decision-making for ongoing evaluation.

When should I avoid using autonomous self-improvement loops for my codebase?▼

You should avoid autonomous self-improvement loops when your codebase lacks clear benchmarking criteria or strict guardrails. The process requires defined goals, sealed files, and trust gates to safely prevent unsafe modifications during automated multi-agent execution.