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florianziesche
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capability-evolver

Analyze runtime history to patch scripts and update memory in OpenClaw workflows.

Updated Aug 27, 2026
Search Tags:#self-improvement#mutation#autonomous-agents#memory-management#self-repair#capability-evolver
One-click install
npx skills add https://github.com/florianziesche/ainary-platform --skill capability-evolver-florianziesche
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: capability-evolver
Source: https://github.com/florianziesche/ainary-platform/tree/main/skills/capability-evolver
Command: npx skills add https://github.com/florianziesche/ainary-platform --skill capability-evolver-florianziesche

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

capability-evolver solves the problem of stagnation in AI agent performance by analyzing runtime history and automatically applying patch-like mutations to improve behavior and avoid local optima.

Core Features & Use Cases

  • Auto-Log Analysis: automatically scans memory and history to find failures and inefficiencies.
  • Self-Repair: patches scripts and memory to improve reliability.
  • Genetic Mutation Protocol: introduces randomized mutation to escape local optima.
  • One-Command Evolution: trigger with /evolve to run a full cycle.

Quick Start

Run /evolve or node skills/capability-evolver/index.js to start an evolution cycle.

Frequently Asked Questions about capability-evolver

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

FAQPage Schema
How do I automate self-improvement for autonomous AI agents?▼

Self-repair for AI agents patches scripts and updates memory based on runtime history analysis, automatically correcting inefficiencies and failures to improve overall reliability without manual intervention.

How do I run an evolution cycle to mutate agent behavior?▼

Run an evolution cycle by executing the /evolve command or running the index.js script directly, which triggers a full loop to apply patch-like mutations and persist changes through the artifact store.

Why does my AI agent performance stagnate and how do I fix it?▼

AI agent performance stagnates when trapped in local optima, which can be fixed by injecting controlled genetic mutations that analyze logs and apply randomized behavioral patches to escape performance plateaus.

Can I use genetic mutation protocols to escape local optima in agent workflows?▼

Yes, genetic mutation protocols introduce randomized mutations into agent workflows to escape local optima, operating across memory, logs, and code to persist changes and publish updates within OpenClaw workflows.

What are the limitations of using automated mutation to evolve agent capabilities?▼

Limitations include relying on runtime history quality for failure analysis, requiring OpenClaw workflows for operation, and needing controlled mutations to prevent unpredictable behavioral shifts when evolving capabilities.

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