EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification (April 2026)
FreeSkill Generator iteratively refines agent skills while a Surrogate Verifier co-evolves to provide actionable feedback without ground-truth; surpasses human-written skills on SkillsBench in 5 rounds; works on Claude Code and Codex
About EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification (April 2026)
CoEvoSkills (Self-Evolving Agent Skills via Co-Evolutionary Verification) is a research framework that enables LLM agents to autonomously generate complex, multi-file skill packages for multi-step professional tasks. It couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without requiring ground-truth test content. On the SkillsBench benchmark, CoEvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and demonstrates strong generalization capabilities to six additional LLMs. The approach addresses the label-intensive nature of manual skill authoring and mitigates human–machine cognitive misalignment, offering a path toward fully autonomous skill development for agentic AI systems.
Key Features
Pros & Cons
- Highest pass rate on SkillsBench compared to five baselines
- Self-evolving without requiring ground-truth or human-labeled data
- Generalizes well across multiple LLMs (Claude Code, Codex, 6 others)
- Addresses the complexity of generating multi-file skill packages
- Open-source framework with code to be released