SkillSpec: Intent-Masked Specification Reasoning for Agent Skill Correctness
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arXiv:2608. 07639v1 Announce Type: cross Abstract: Agent Skills provide reusable capabilities to LLM agents.
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It first checks functional claims against evidence and evaluates artifacts against nine safety properties, then tests admitted skills in a controlled environment to capture execution traces and identify failures. The approach achieves perfect precision and recall in vulnerability detection, significantly reduces attack success rates, and boosts task effectiveness and security rates in skill generation benchmarks.
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It evaluates candidate skills against source evidence and nine safety properties, then tests them in a controlled environment to capture execution traces and identify failures. The system iteratively refines skills based on these results, achieving high precision in vulnerability detection and significantly improving task performance and security rates.
arXiv:2607. 02345v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents increasingly automate software engineering tasks through reusable skills, natural-language instruction documents that guide planning and execution.
arXiv:2608.23417v1 Announce Type: new Abstract: Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at infere...
arXiv:2609.33772v2 Announce Type: replace Abstract: Executable environments are critical for post-training agents on tasks that require tool use and multi-step interaction, but constructing executabl...