From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

2026-05-22

AI Summary

Existing research lacks a systematic understanding of the full lifecycle of model-generated skills—spanning experience generation, skill extraction, and skill consumption—making it difficult to evaluate their effectiveness and applicability. This work proposes the first utility-based evaluation framework to systematically analyze key factors influencing skill extraction and consumption across five task domains. Through multi-model comparisons, cross-consumer transfer tests, and analyses of experience composition, we find that extracted skills are on average beneficial but exhibit significant negative transfer, with utility independent of model scale. Furthermore, we introduce a meta-skill guidance strategy that substantially improves cross-domain skill quality and mitigates negative transfer, revealing a notable inconsistency between extractor and consumer performance.

Abstract

Language agents increasingly improve by reusing \emph{skills} -- structured procedural artifacts distilled from past experience. In particular, \emph{domain-level} and \emph{model-generated} skills are especially promising. They offer fast adaptation within a domain by encoding domain-specific recurring procedures, and they scale beyond labor-intensive hand-crafting. However, while extraction methods continue to proliferate, understanding remains limited, with no comprehensive study spanning the full skill lifecycle -- \textbf{experience generation}, \textbf{skill extraction}, and \textbf{skill consumption} -- to ask whether such skills actually work, when they work, and what makes them succeed or fail. To close this gap, we build a utility-grounded evaluation framework that provides systematic experimental results across extractors and target agents, covering five diverse agentic task domains. We find that model-generated skills are beneficial on average but exhibit non-trivial negative transfer, and that neither extractors nor targets behave uniformly. A model can be a strong extractor yet a weak consumer, or vice versa, with skill utility independent of model scale or baseline task strength. To explain these patterns, we then dissect each lifecycle stage in depth, analyzing how experience composition shapes skill quality, what properties characterize useful skills, and how the same skill transfers across different consumers. Finally, we translate these findings into a concrete \emph{meta-skill} that guides skill extraction toward the features tied to actual utility, which consistently improves skill quality across domains and substantially reduces negative transfer.

Problem

• skill extraction
• skill consumption
• language agents
• negative transfer
• model-generated skills

Innovation

• model-generated skills
• skill lifecycle
• negative transfer
• utility-grounded evaluation
• meta-skill