COLLEAGUE.SKILL introduces an automated system that distills heterogeneous traces of human expertise and role-specific knowledge into portable, inspectable, and usable AI skill packages.

SkillAdaptor is a novel, training-free framework that enables stable, step-level adaptation of external skills for LLM agents by precisely attributing failures to specific skills.

This paper introduces SkillComposer, a method for structured skill composition in LLM agents, which predicts an executable skill plan that jointly specifies the activated subset, count, and execution…

ReSkill is an RL-in-the-loop framework that reconciles skill creation and policy optimization by automatically creating, testing, and refining modular skills alongside the agent's policy learning, lea…

The paper introduces MASA, a model-aware skill alignment framework that adaptively rewrites general and task-specific skills for LLM agents, achieving superior performance across diverse backbones and…

SkillTrojan introduces a novel backdoor attack targeting the composition of reusable skills in agent systems, demonstrating high attack success rates with minimal impact on normal system functionality…

This paper proposes Hypothesis-Driven Skill Optimization (HDSO), a train-free framework for improving action-oriented LLM agents with external skills while preserving their frozen inference endpoints.

The paper introduces SkillCenter, a large open skill library for AI agents, with over 216,000 skills from various sources, and presents an end-to-end framework for acquiring, filtering, generating, gr…

This paper proposes SkillOpt-Lite, a minimal viable pipeline for skill optimization in autonomous agents, which accelerates convergence and outperforms full SkillOpt.

SkillAttack is a red-teaming framework that dynamically tests the exploitability of latent vulnerabilities in LLM agent skills using adversarial prompting, demonstrating that even benign skills pose s…

This paper measures the impact of procedural skills on LLM agents, distinguishing between improvements and regressions, and identifies causes of regression.

This paper demonstrates that the natural language metadata (SKILL.md) used to describe AI agent skills introduces significant semantic supply-chain risks, allowing attackers to manipulate discovery, s…

The paper introduces Proteus, a self-evolving red-team framework that measures the adaptive leakage risk of LLM agent skills, demonstrating that current vetting methods significantly underestimate res…

SkillRevise is an execution-grounded framework that iteratively refines initial, imperfect LLM agent skills by diagnosing defects from execution evidence and applying empirically validated edits, sign…

The paper proposes HiSME, a lightweight hierarchical skill meta-evolving solution that jointly optimizes skills and the skill evolving strategy by learning meta-skills from task execution traces, lead…

The paper introduces MMG2Skill, a closed-loop framework that converts noisy, human-oriented web guides into editable, executable skills, significantly improving agent performance across diverse tasks.

SkillTester is a comprehensive tool and framework designed to benchmark both the functional utility and the security robustness of agent skills, providing standardized scores and status labels.

This paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework for LLM training that bridges the gap between structured verification and open-ended exploration.

SkillSmith is a synergy-aware framework that jointly co-evolves skills and tools, significantly improving self-improving agent systems by modeling skill-tool interactions and diagnosing failures.

The paper introduces SkillSafetyBench, a comprehensive benchmark demonstrating that agent safety failures often stem from adversarial influences within reusable skills and execution environments, rath…