What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills
Quick Answer
The paper introduces SkillSV, a structure-aware Shapley-style framework for skill valuation in AI agents, distinguishing internal skill units based on their dependencies and hierarchy.
Quick Take
It evaluates skill contributions through paired deletion and length-neutral padding, achieving fidelity and actionability across four benchmarks, thus enabling effective pruning and compression of agent skills.
Key Points
- SkillSV evaluates structured skill units, considering dependencies and document hierarchy.
- Utilizes paired deletion and length-neutral padding to isolate content value from context cost.
- Demonstrates effectiveness in four agentic benchmarks for skill evaluation.
- Facilitates safe pruning and compression of agent skills without losing aggregate performance.
- Improves understanding of internal skill contributions in automated feedback loops.
DeepSignal Analysis
What happened
The paper presents SkillSV, a framework for evaluating the value of skills in AI agents. It focuses on the internal structure of skills, such as dependencies and hierarchies, to assess their contributions effectively. The framework employs techniques like paired deletion and length-neutral padding to distinguish content value from context cost.
Key evidence
- SkillSV evaluates skill contributions by compiling skills into units, dependencies, and hierarchies, ensuring only valid counterfactual skills are assessed.
- The framework uses paired deletion and length-neutral padding to separate content value from context cost, enhancing the accuracy of skill valuation.
- SkillSV was tested on four benchmarks, demonstrating its ability to recover unit interactions and guide safe pruning and compression of agent skills.
Why it matters
Understanding the value of individual skill components in AI agents is crucial for optimizing their performance and efficiency. SkillSV's structured approach allows for more precise skill valuation, which can lead to better decision-making in skill pruning and compression. This can ultimately enhance the overall effectiveness of AI systems in various applications.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Agent skills are increasingly optimized by automated feedback loops, producing long structured artifacts whose internal value remains unclear. We study skill valuation: assigning credit to the internal units of a fixed skill, such as rules, examples, scripts, and heuristics, under a fixed agent and held-out task distribution. Skill valuation differs from data or prompt-span valuation because skill units are structured: they may depend on other units, belong to a document hierarchy, trigger agent behavior, and consume limited prompt context. We introduce SkillSV, a structure-aware Shapley-style framework for skill valuation. SkillSV compiles a skill into units, dependencies, and hierarchy, so that only valid counterfactual skills are evaluated. It uses paired deletion and length-neutral padding to separate content value from context cost, and estimates the resulting values with a rollout-budgeted estimator for noisy agent evaluations. On four agentic benchmarks, we assess the faithfulness, actionability, and explanation of SkillSV: it recovers unit interactions, preserves aggregate skill lift, and guides safe pruning and compression.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.04562 [cs.AI] |
| (or arXiv:2608.04562v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.04562 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Junfeng Liu [view email]
[v1]
Wed, 5 Aug 2026 07:56:26 UTC (1,896 KB)
— Originally published at arxiv.org
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