478 GUI skills push GPT-5's desktop success rate up 15.6 points

Notes on CUA-Skill: Develop Skills for Computer Using Agent (arXiv:2601.21123) — Tianyi Chen, Yinheng Li, Michael Solodko, Sen Wang, Nan Jiang, T. Cui, Junheng Hao, Jongwoo Ko, Sara Abdali, Suzhen Zheng, Leon Xu, Hao Fan, and 3 more · January 2026

Note published July 23, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

AI-assisted notes · reviewed by SkillFed Skill retrieval

CUA-Skill turns human computer-use know-how into 478 atomic, parameterized skills across 17 Windows applications — File Explorer, Excel, Word, Chrome, VS Code, PowerPoint, and more. Each skill pairs a natural-language intent with an argument pool and a parameterized execution graph, a directed graph whose nodes are control states and edges are base GUI actions gated by UI predicates. A separate composition graph encodes which skills can legally follow which, turning a flat skill list into a navigable map of higher-level tasks. Built on top, CUA-Skill Agent handles the runtime work: hybrid lexical-and-semantic skill retrieval (Qwen3-Embedding-0.6B, top-5 candidates) narrows the catalog to candidates, argument instantiation treats enumerable and open-ended inputs differently, and a memory log of past attempts drives failure recovery — falling back to raw primitive actions when nothing cataloged fits.

The library's payoff scales with the backbone model — it isn't a fixed lift. Adding CUA-Skill to Qwen3-VL-32B raised task success from 6.5% to 11.8% (+5.2 points); to GPT-4o from 19.6% to 28.1% (+8.5 points); to GPT-5 from 34.6% to 50.3% (+15.6 points) — same 478 skills, three different lifts. Wired into the full CUA-Skill Agent on GPT-5, the system reaches 50.3% single-attempt and 57.5% best-of-three on WindowsAgentArena, a new state of the art ahead of AgentS3's 56.6% best-of-three and well clear of OpenAI's Operator at 37.4%, while invoking only 117 of the library's 478 skills and using fewer execution steps than the systems it beats. Human performance on the same benchmark sits at 74.5%, so a real gap remains.

Key numbers

Skill library scale	478 skills across 17 Windows apps
GPT-5 success rate, no skills → with skills	34.6% → 50.3% (+15.6 pts)
WindowsAgentArena, best-of-three	57.5% (new SOTA)
Human baseline on WindowsAgentArena	74.5% vs agent's 57.5%
Skills actually invoked in eval	117 of 478

What this means for skill authors

The structural bet is the interesting part: instead of prompting a model to reason out low-level GUI actions from scratch on every task, CUA-Skill pre-compiles that reasoning once and reuses it as a graph. The composition graph is the piece most agent frameworks skip — an explicit map of which skills can legally follow which, rather than leaving sequencing to whatever the model infers from a system prompt.

The ablation table is the number worth remembering if you build or vet skills: the same 478-skill library adds +15.6 points to GPT-5 but only +5.2 to Qwen3-VL-32B. A skill isn't a fixed multiplier on capability — the backbone sets the ceiling, and a library tuned against one model's blind spots may barely move a weaker one. Test any nontrivial skill against more than one target model before trusting its reported lift.

It also surfaces a coverage problem every skill catalog eventually hits: 478 skills exist, but only 117 ever got invoked across the benchmark. A large library isn't automatically a useful one if retrieval only ever surfaces a sliver of it. That's the case for search and vetting that rank skills by task fit — not catalogs that just keep growing.

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Related notes

References

CUA-Skill: Develop Skills for Computer Using Agent (arXiv:2601.21123)
WindowsAgentArena benchmark
Agent-S2 (computer-use agent baseline)
UFO-2 (computer-use agent baseline)
OpenAI Operator (computer-use agent baseline)