self-improve

Run autonomous codebase improvement loops with deterministic benchmark scoring.

Updated May 20, 2026
One-click install
npx skills add https://github.com/xdkp/oh-my-claudecode --skill self-improve-xdkp
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: self-improve
Source: https://github.com/xdkp/oh-my-claudecode/tree/main/skills/self-improve
Command: npx skills add https://github.com/xdkp/oh-my-claudecode --skill self-improve-xdkp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires node, python3, jq, and includes scripts (resource) components.

What problem does it solve?

It removes the manual, trial-and-error burden of improving a codebase by running an end-to-end loop that proposes, implements, and objectively benchmarks multiple competing changes until a measurable improvement is found.

Core Features & Use Cases

  • Autonomous improvement loop: Orchestrates research, planning, architecture review, critic gating, execution, and tournament selection without pausing for confirmation mid-run.
  • Benchmark-first evolution: Builds/uses a deterministic benchmark, runs repeated evaluations, and ranks candidates by score direction while enforcing improvement/hold rules to prevent regressions.
  • Safety and integrity guardrails: Uses sealed-file enforcement via validate.sh to prevent the loop from modifying benchmark evaluation code, and applies structured JSON contracts between agents (plans, research briefs, and results).
  • Resumable, stateful workflow: Tracks progress per iteration and supports crash recovery via per-iteration state and worktree cleanup.

Quick Start

Start a Claude Code / OMC session and run the self-improve setup flow so the loop can create a goal, build/confirm a benchmark, and then begin autonomous iterations on your target repository.

Frequently Asked Questions about self-improve

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

FAQPage Schema
How do I autonomously improve code using benchmark scoring?

You prevent regression during automated code optimization by enforcing improvement/hold rules and using sealed-file validation, ensuring the loop cannot modify benchmark evaluation code while confirming no regressions before merging tournament winners.

How does tournament selection work for iterative codebase optimization?

You prevent regression during automated code optimization by enforcing improvement/hold rules and using sealed-file validation, ensuring the loop cannot modify benchmark evaluation code while confirming no regressions before merging tournament winners.

Do I need git worktrees to run autonomous code improvement loops?

You prevent regression during automated code optimization by enforcing improvement/hold rules and using sealed-file validation, ensuring the loop cannot modify benchmark evaluation code while confirming no regressions before merging tournament winners.

Can I use autonomous agents to optimize a specific metric in a local repository?

You prevent regression during automated code optimization by enforcing improvement/hold rules and using sealed-file validation, ensuring the loop cannot modify benchmark evaluation code while confirming no regressions before merging tournament winners.

What are the limitations of using tournament selection for code improvement?

You prevent regression during automated code optimization by enforcing improvement/hold rules and using sealed-file validation, ensuring the loop cannot modify benchmark evaluation code while confirming no regressions before merging tournament winners.