SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents

• URL: http://arxiv.org/abs/2608.13173v1
• Date: Thu, 13 Aug 2026 12:41:03 GMT
• Title: SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents
• Abstract summary: Existing agent skills are primarily created through human manual crafting or agent execution traces.<n>We propose SkillShapley, a step-level attribution framework for agent skills.<n> Experiments on skills from the widely adopted SkillsBench demonstrate that SkillShapley can effectively and efficiently identify high- or low-value skill steps.
• Score: 7.7046487215582475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Existing agent skills are primarily created through human manual crafting or agent execution traces, with limited understanding of how each step contributes to overall skill performance on specific tasks; i.e., there remains an open problem in quantifying the contribution of individual steps within an agent skill. To address this issue, we first model skill-step attribution as a Shapley value-based contribution estimation problem, and then propose SkillShapley, a step-level attribution framework for agent skills. Notably, SkillShapley operates in two phases, motivated by key empirical insights, i.e., discretized benchmark rewards that create sharp performance cliffs, and step interactions that are largely additive rather than synergistic. Specifically, it first identifies informative coalitional regions, and then adaptively samples new coalitions that can yield reusable marginal evidence. Experiments on skills from the widely adopted SkillsBench demonstrate that our SkillShapley can effectively and efficiently identify high- or low-value skill steps, providing several key takeaways for agent skill creation.

Related papers

• SkillAlchemy: Open-World Agent Skill Creation [11.836010370630568]
  Agent skills are reusable procedural artifacts that extend language agents with specialized, tool conventions, and domain behaviors at inference time.<n>Create reliable skills still depends largely on human authorship, model priors, or execution traces.<n>We propose SkillAlchemy, an admission-centered framework for source-grounded skill creation.
  arXiv  Detail & Related papers  (2026-08-24T15:58:57Z)
• Skill-Use: Can LLMs Actually Use Skills in Agentic Harnesses? [38.776828282073865]
  We introduce Skill-Use, a benchmark that evaluates skill use under progressive disclosure.<n> Trigger measures whether the agent invokes the relevant skill, Compliance measures how faithfully it follows the prescribed procedure, and Boundary measures whether it avoids forbidden operations.<n>A Skill-Use (SU) score combines the three and credits execution only after the skill is triggered.
  arXiv  Detail & Related papers  (2026-08-05T13:29:16Z)
• Generative Skill Composition for LLM Agents [58.47191648555256]
  LLM agents benefit from skills for solving complex tasks.<n>As skill libraries grow, selecting an appropriate skill composition has emerged as a central bottleneck.<n>We propose SkillComposer, which instantiates structured skill composition as task-conditioned skill sequence prediction.
  arXiv  Detail & Related papers  (2026-06-30T17:53:09Z)
• SkillAudit: From Fixed-Suite Benchmarking to Skill-Centered Assessment [10.850946672552277]
  We introduce SkillAudit, an end-to-end framework for skill-centered assessment.<n>It generates a comprehensive, multi-dimensional evaluation report spanning utility, efficiency/cost, and safety.<n>After scanning top-ranked real-world skill packages, we found that over 7% of skills are at risky status.
  arXiv  Detail & Related papers  (2026-06-21T17:40:05Z)
• A Framework for Evaluating Agentic Skills at Scale [0.4509560087514856]
  Agent skills are structured, reusable knowledge artifacts that augment agent capabilities.<n>No reusable methodology exists for evaluating an individual skill.<n>We present an evaluation framework that lets a skill author construct realistic tasks.
  arXiv  Detail & Related papers  (2026-06-16T11:46:56Z)
• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs [70.1970574147839]
  We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph.<n>SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making.<n> Experiments show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods.
  arXiv  Detail & Related papers  (2026-05-12T12:21:49Z)
• Skill-R1: Agent Skill Evolution via Reinforcement Learning [84.35984979949502]
  Skill-R1 is a reinforcement learning framework for instance-level recurrent skill optimization from verifiable rewards.<n>It preserves black-box compatibility with both open- and closed-source models while making adaptation substantially cheaper than model-level updates.
  arXiv  Detail & Related papers  (2026-05-10T06:19:15Z)
• SkillGen: Verified Inference-Time Agent Skill Synthesis [60.927977774369516]
  SkillGen is a framework that synthesizes a single auditable skill from trajectories generated by a base agent.<n>It identifies reusable success patterns, recurring failure modes, and behaviors that appear in nearby successes but are missing from failures.<n>A key novelty in SkillGen is that we model agent skills as interventions to empirically verify the net effect of skills on the overall performance.
  arXiv  Detail & Related papers  (2026-05-09T19:24:11Z)
• SkillMaster: Toward Autonomous Skill Mastery in LLM Agents [27.651128308229378]
  We propose SkillMaster, a training framework that teaches agents to create new skills, refine existing skills, and select accumulated skills during task solving.<n>First, we train agents through trajectory-informed skill review, teaching agents to propose, update, or retain skills based on evidence from completed episodes.<n>Second, each candidate skill edit is designed to be evaluated by its counterfactual utility on related probe tasks, providing a direct learning signal for training skill-editing decisions.<n>Third, we introduce DualAdv-GRPO, which separately estimates advantages for task-solving actions and skill-editing decisions, stabilizing joint training across task solving and
  arXiv  Detail & Related papers  (2026-05-09T05:03:00Z)
• EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification [85.3692584167951]
  Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks.<n>A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts.<n>EvoSkills is a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages.
  arXiv  Detail & Related papers  (2026-04-02T06:43:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.