capability-evolver

Name: capability-evolver
Availability: InStock
Author: Ethan

Analyze runtime history to identify failures and propose targeted agent evolutions.

65|9|Updated Jan 29, 2026

Search Tags:#automation #self-improvement #audit #protocol #evolution #ai-agent

Ready to run

npx skills add https://github.com/EthanAlgoX/MarketBot --skill capability-evolver-ethanalgox

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: capability-evolver
Source: https://github.com/EthanAlgoX/MarketBot/tree/main/marketbot/skills/capability-evolver
Command: npx skills add https://github.com/EthanAlgoX/MarketBot --skill capability-evolver-ethanalgox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

Identifies failures and inefficiencies in runtime behavior and generates targeted evolutions to improve robustness and efficiency.

Core Features & Use Cases

Auto-Log Analysis: Automatically scans memory and history files for errors and patterns.
Self-Repair: Detects crashes and suggests patches.
GEP Protocol: Standardized evolution with reusable assets.
One-Command Evolution: Just run /evolve.
Use Case: When the agent experiences repeated failure in a skill or memory leak, it evolves code or memory to address it.

Quick Start

Run the /evolve command to begin an automated 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 AI agents that experience repeated runtime failures?▼

Automating self-improvement for AI agents requires analyzing runtime history to identify failures and propose targeted evolution. This protocol-constrained workflow scans memory and history files for errors, detecting crashes and suggesting patches to improve robustness.

What is the GEP protocol for AI agent evolution and how does it work?▼

The GEP protocol is a standardized evolution framework that uses reusable assets like genes and capsules to safely patch AI agents. It applies runtime monitoring, memory updates, and schema refinements across multiple skill modules within a protocol-constrained workflow.

How to patch memory leaks and crashes in MarketBot agents automatically?▼

To patch memory leaks and crashes in MarketBot agents automatically, run an evolution cycle that scans runtime memory and history files to detect errors. The system then generates targeted code or memory patches to address the specific failure.

Do I need an auditable evolution protocol and asset store to use automated agent patching?▼

Yes, automated agent patching requires an auditable evolution protocol and an asset store for reusable genes and capsules. It also requires integration with the market bot's tooling to execute safe, tracked changes across multiple skill modules.

Why does my AI agent self-repair process need schema refinements and runtime monitoring?▼

Your AI agent self-repair process needs schema refinements and runtime monitoring to identify inefficiencies and prevent repeated failures. Analyzing runtime behavior allows the protocol to generate targeted evolutions that improve overall robustness and efficiency.

Best way to execute safe tracked changes across multiple AI skill modules?▼

The best way to execute safe tracked changes across multiple AI skill modules is using a protocol-constrained workflow with an auditable evolution protocol. This ensures that runtime patching, memory updates, and schema refinements are tracked and reusable.