skill-evolution

Mutate skill definitions and measure fitness across agent generations.

60|13|Updated Dec 22, 2025
One-click install
npx skills add https://github.com/plurigrid/asi --skill skill-evolution
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-evolution
Source: https://github.com/plurigrid/asi/tree/main/skills/skill-evolution
Command: npx skills add https://github.com/plurigrid/asi --skill skill-evolution

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Patterns for evolutionarily robust skills that adapt across agent generations (Darwin-Godel).

Core Features & Use Cases

  • Fitness signals: Validation and compatibility checks.
  • Mutation & Selection: Evolving skills through variation and selection.
  • Cross-platform reuse: Recombination of patterns across contexts.

Quick Start

Run an evolution routine on a population of skills and observe survivors.

Frequently Asked Questions about skill-evolution

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

FAQPage Schema
How do I evolve skills across agent generations to improve compatibility?

Skill evolution develops adaptive skill definitions through mutation, validation, and fitness scoring across agent activations. The process mutates skill bodies, validates quality through compatibility checks, and measures fitness by tracking activation rates and token efficiency to produce evolutionarily robust skills suited for cross-platform deployment.

What mutation operators can I apply to refine skill definitions?

Mutation operators include description refinement, body compression, triadic rebalancing, and cross-pollination. These operators systematically vary skill definitions while maintaining functional integrity, executed under CI validation pipelines to ensure quality and compatibility across platform boundaries.

Can I use skill evolution for cross-platform agent ecosystems?

Yes. Skill evolution enables cross-platform reuse by recombining skill patterns and tracking compatibility across contexts. It measures activation rates, token efficiency, and fitness signals to validate that mutated skills function reliably when deployed across different agent platforms and updated environments.

How do fitness signals and validation work in skill selection?

Fitness signals measure skill performance through validation checks and compatibility scoring across activations. Selection filters skills based on these metrics—activation rates, token efficiency, and platform compatibility—to identify survivors that adapt successfully across agent generations.

What happens to skills during platform updates in an evolving ecosystem?

During platform updates, evolved skills are re-evaluated against new compatibility and fitness requirements. Mutation operators and CI validation pipelines ensure that skill definitions adapt or are filtered out based on activation success, maintaining ecosystem robustness across platform versions.