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From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills research paper by Microsoft, 2026

Microsoft · May 22, 2026 · Agents and evaluation · 15 citations · 26 upvotes · unverified

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What it shows

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.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Zisu Huang, Jingwen Xu, Yifan Yang and 13 more
arXiv
2605.23899 · PDF
Venue
arXiv.org
Citations
15, 2 influential · Semantic Scholar
Upvotes
26 · Hugging Face
Code
github.com/microsoft/SkillLens
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 2Sep 25, 2026
Citationsfirst count: 15Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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