arXiv cs.MA· Achuth Chandrasekhar, Jesse Barkley, Peter Pak, Rumi Loghmani, Aakash Oza, Amir Barati Farimani·· 11 小时前AI 评分48
机械工程技能：面向设计、分析与制造的开源 Agent 技能库
Mechanical Engineering Skills: An Open Library of Agent Skills for Design, Analysis and Manufacturing
AI 导读
研究团队发布 Mechanical Engineering Skills 开源库，包含 33 个 Agent 技能，每个技能整合操作说明、可执行工具与验证流程，由语言模型调度、确定性工具执行计算。
正文 · 原文
Abstract:Mechanical engineers use specialized tools for CAD, simulation, manufacturing and research, each with a distinct interface and learning curve. We present Mechanical Engineering Skills, an open library of 33 agent skills that makes these capabilities accessible through a single text or voice conversation. Each skill combines operating instructions, executable tools and verification procedures. A language model interprets the request, selects and coordinates skills, while deterministic tools perform computations and return checked results. Default workflows require no tool-specific interface training, local model execution or model fine-tuning; skills load on demand and install with one command. Nineteen skills adapt published or preprint research from our laboratory, including text-to-CAD, linkage synthesis, CFD setup, stress-field and lattice surrogates, equation discovery, additive-manufacturing planning and in-process monitoring. Fourteen additional skills support core engineering and education. We describe the skill format, design principles and a method for translating research into practical workflows. Five case studies with explicit evidence scopes and independent reruns demonstrate exact analytical agreement for lattice homogenization, rocket apogee predictions within 4% of stored OpenRocket references, an equation fit nine times more accurate than a cubic polynomial at an unseen temperature, and an end-to-end rehearsal of porosity monitoring. Controlled agent comparisons remain future work. The open library is available at this https URL.
| Subjects: | Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.05758 [cs.MA] |
| (or arXiv:2611.05758v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05758 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Achuth Chandrasekhar [view email]
[v1]
Tue, 3 Nov 2026 23:57:51 UTC (582 KB)
来源：arXiv cs.MA · arxiv.org