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

Hugging Face Daily Papers Papers
model-generated-skills skill-extraction skill-consumption language-agents negative-transfer systematic-study

Summary

This paper systematically evaluates model-generated skills for language agents across the full lifecycle of experience generation, extraction, and consumption, finding that skills are beneficial on average but exhibit non-trivial negative transfer, leading to a meta-skill that improves skill quality.

Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level and 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 -- experience generation, skill extraction, and 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 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.
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Abstract

Language agents benefit from reusable skills that encode domain-specific procedures, but their effectiveness varies significantly across different extraction and consumption scenarios, requiring careful evaluation and meta-skill guidance to optimize performance.

Language agentsincreasingly improve by reusingskills-- structured procedural artifacts distilled from past experience. In particular, domain-level andmodel-generated skillsare 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 -- experience generation,skill extraction, andskill consumption-- to ask whether suchskillsactually 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 thatmodel-generated skillsare beneficial on average but exhibit non-trivialnegative 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 usefulskills, and how the same skill transfers across different consumers. Finally, we translate these findings into a concretemeta-skillthat guidesskill extractiontoward the features tied to actual utility, which consistently improves skill quality across domains and substantially reducesnegative transfer.

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