This survey analyzes the emerging field of Agent Skills, defining a formal skill model, categorizing acquisition pathways, detailing retrieval strategies, and outlining a five‑stage evolution process, while highlighting large‑scale skill repositories and their implications for AI product design.
SkillClaw introduces a centralized evolution framework that transforms user interactions into structured evidence, allowing LLM agents to refine, create, or skip skills based on aggregated success and failure patterns, with nightly validation ensuring only proven improvements are deployed, resulting in consistent performance gains across diverse tasks.
EverOS launches a global beta of its next‑generation memory infrastructure that lets autonomous agents automatically extract experience, cluster it semantically, and evolve reusable skills, boosting OpenClaw task success rates by up to 234.8% while addressing context‑window limits, multimodal retrieval, and developer transparency.
The EvoSkill framework introduced by Sentient and Virginia Tech researchers equips large language models with a text‑feedback loop that automatically discovers, refines, and validates reusable agent Skills, boosting task‑specific accuracy by 12.1% and enabling cross‑domain transfer without altering the underlying model parameters.
