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.