arXiv AI
Jul 14
Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries
arXiv:2607. 10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model.
Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
arXiv:2606. 09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories.
Related stories
arXiv AI
Jun 16
Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents
arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.
arXiv AI
Jun 9
Skill Retrieval Augmentation for Agentic AI
arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.
arXiv AI
Aug 7
Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning
arXiv:2608. 05245v1 Announce Type: new Abstract: Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-evolution in expert domains.
arXiv AI
Jun 6
Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
arXiv:2603. 25158v5 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on domain-specific skills, yet manually authoring such skills does not scale, and skills generated purely from parametric knowledge often miss critical operational pitfalls.
arXiv AI
Jun 11
SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior
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.
arXiv AI
Aug 10
SkillEval: Decomposing Agent Skill Quality into Interpretable Signals
arXiv:2608. 06891v1 Announce Type: new Abstract: Agent skills provide reusable procedural knowledge that helps agents solve specialized tasks.
arXiv AI
Jun 17
A Framework for Evaluating Agentic Skills at Scale
arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.
Hugging Face Trending Papers
Jun 10
SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior
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. We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a normalized flat baseline.
arXiv AI
Jun 2
SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.
arXiv AI
Aug 12
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
arXiv:2604. 01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address.
arXiv AI
Aug 11
SkillSentry: Reliable Skill Execution for LLM Agents via Runtime Assurance
arXiv:2608. 09253v1 Announce Type: new Abstract: LLM agents are increasingly equipped with skills to perform complex tasks through multi-step reasoning and tool use.