SkillShapley puts a Shapley value on each step of an agent skill, and finds step interactions are mostly additive

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Agent skills — the SKILL.md-style procedural instructions now shipping across coding and document agents — are written by hand or scraped from execution traces with no measurement of which steps actually help. SkillShapley (arXiv 2608.13173, 2026-08-13) models step attribution as Shapley value estimation and exploits two empirical observations to make it affordable: benchmark rewards are discretized so performance shows sharp cliffs, and step interactions are largely additive rather than synergistic. It first identifies informative coalitional regions and then adaptively samples coalitions that yield reusable marginal evidence, and on skills from SkillsBench it separates high- from low-value steps — a concrete way to prune bloated skill files instead of guessing.