arXiv:2608. 10046v1 Announce Type: new Abstract: Soft skills shape collaboration among ML engineers, data scientists, and software engineers building ML-enabled systems, yet what we know about them comes almost entirely from the demand side.
arXiv:2610.20186v1 Announce Type: new Abstract: Skills are widely assumed to play a central role in career transitions, motivating their use in career path prediction (CPP) systems. In this work, we...
arXiv:2608. 14036v1 Announce Type: new Abstract: Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge.
arXiv:2608. 04077v1 Announce Type: new Abstract: Evaluating financial AI agents requires criteria aligned with real professional work.
The paper introduces FinSkillBench, a benchmark of 2,603 financial AI agent episodes across portfolio construction, risk management, and fundamental analysis, evaluated with deterministic verifiers. Experiments on 9 models and 3 resource settings show that curated skill packages improve performance by +16.2 points, while in‑episode skill generation adds only +0.5 points. Decomposing the premium reveals that executable tools contribute +19.5 points, documentation +5.6 points, and their combination is subadditive, with the effect varying by workflow type.
arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.
arXiv:2608. 06891v1 Announce Type: new Abstract: Agent skills provide reusable procedural knowledge that helps agents solve specialized tasks.
The paper introduces a structured approach to extracting skill and responsibility level pairs from free text using the Skills Framework for the Information Age (SFIA). It evaluates five methods—including lexical baselines, retrieval‑augmented generation, and multi‑agent crews—against expert‑mapped European ICT role profiles, finding that generative strategies are more precise and that only explicit level‑prediction strategies reliably assign responsibility levels. The study also releases an automated SFIA‑9 corpus and establishes the first reproducible baseline for level‑aware skill extraction.
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.
arXiv:2607. 18785v1 Announce Type: new Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
arXiv:2606. 30775v1 Announce Type: cross Abstract: Enterprise AI agents route user queries to specialized skills by matching queries against natural language skill descriptions.
arXiv:2606. 11543v1 Announce Type: new Abstract: Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized.