What Keeps Agent Skills from Being Reusable? Evidence from 138K SKILL.md Files

• URL: http://arxiv.org/abs/2608.08453v1
• Date: Sun, 09 Aug 2026 03:49:30 GMT
• Title: What Keeps Agent Skills from Being Reusable? Evidence from 138K SKILL.md Files
• Abstract summary: Many public SKILL.md files appear to originate from a single task, repository, or conversation.<n>We analyze this gap across 138,133 public SKILL.md files from 20,556 repositories.<n>We find that 91.8% of skills contain at least one detected defect, with stable estimates across lenient and strict thresholds.
• Score: 12.451393173022346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Under the current standard, Agent Skills are SKILL.md files that combine instructions with supporting resources, enabling Large Language Model (LLM) agents to reuse procedures beyond a single conversation. Yet many public skills appear to originate from a single task, repository, or conversation, even when they are shared as reusable components. We analyze this gap across 138,133 public SKILL.md files from 20,556 repositories using a two-tier defect taxonomy grounded in the official specification and best-practice guidance. We find that 91.8% of skills contain at least one detected defect, with stable estimates across lenient and strict thresholds (88.8-94.6%). The dominant failures are ordinary packaging problems rather than exotic attacks: weak routing metadata, bloated or non-actionable bodies, and poor resource organization. A deterministic routing stress test over 20,000 skills shows the functional impact: skills with valid routing metadata are retrieved more reliably from startup descriptions than skills with routing defects. Defect rates vary by platform and provenance: specification-aware skills contain fewer defects, while AI-marked skills show more safety and portability problems. Lightweight enforcement and repair experiments support a quality-assured generation workflow combining spec-aware prompting, lightweight linting, automated repair, and safety gating.

Related papers

• Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
  Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
  arXiv  Detail & Related papers  (2026-09-14T17:44:51Z)
• Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills [9.077159743623076]
  repository-native framework for evaluating skills and product capability packages as executable agent artifacts.<n>ACES runs paired live trials with and without a target skill, grades six default runtime metrics, and reports Skill Lift.<n>Open-source implementation of the methodology is available in NVIDIA SkillEvaluator.
  arXiv  Detail & Related papers  (2026-08-20T23:26:14Z)
• Demystifying Agent Skills: Why They Work-Until They Don't [58.92875630998418]
  Existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: emphtextbfWhen do skills help, why do they work, and where do they fail?<n>We isolate the effects of representation, outcome annotation, retrieval difficulty, and cross-framework of skills.<n>We consolidate these observations into a taxonomy of three high-level categories and twelve skill-use modes.
  arXiv  Detail & Related papers  (2026-08-14T07:26:38Z)
• Agent Skills Can Be Harmful: An Empirical Study of Skill-Induced Failures in LLM Agents [9.71657681051469]
  This paper presents a comprehensive analysis of skill-induced agent failures by attributing task failures and cost regressions to specific loaded skills.<n>We instantiate this framework on SkillsBench and SWE-Skills-Bench, yielding 307 skill-induced failures, including 125 functional failures and 182 efficiency regressions.<n>We propose research topics and tooling improvements for safer and more cost-aware skill reuse.
  arXiv  Detail & Related papers  (2026-08-12T10:15:19Z)
• SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills [15.716453891551057]
  We introduce SkillLogic, a framework for analyzing logical relations in skill files.<n>Using SkillLogic, we scan over 5000 public skills and find that 70% contain at least one logical relation.<n>We then construct SLBench, an 86-case executable benchmark from high-confidence, high-impact, and locally testable relations.
  arXiv  Detail & Related papers  (2026-07-10T00:48:30Z)
• SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing [81.51044612408793]
  SkillAudit is a framework for evolving agent skills without ground-truth feedback.<n>To turn behavioral differences into edit guidance, SkillAudit uses Process-Aligned Contrastive Evaluation.<n>It routes edits through two pipelines: Refine removes noisy or irrelevant guidance from broadly useful skills, while Repair replaces passages that conflict with the task.
  arXiv  Detail & Related papers  (2026-06-12T08:20:09Z)
• SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior [23.11308948097101]
  Current benchmarks rarely distinguish what a Skill says from how it is organized.<n>We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand.<n>We present SkillJuror, a framework for evaluating Skill writing paradigms.
  arXiv  Detail & Related papers  (2026-06-10T01:11:50Z)
• What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents [58.87681796862133]
  We study skill rewriting through an economic lens.<n>Our framework profiles skill structure and rewrites skills using information-preservation strategies.<n> Experiments on SkillsBench reveal distinct quality--cost trade-offs across strategies.
  arXiv  Detail & Related papers  (2026-06-08T12:36:51Z)
• COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation [58.84646485020439]
  We present an automated trace-to-skill distillation system for generating person-grounded AI skills.<n> COLLEAGUE.SKILL produces a versioned skill package with two tracks: a capability track for practices, mental models, and decisions, and a bounded behavior track for communication style, interaction rules, and correction history.
  arXiv  Detail & Related papers  (2026-05-29T12:59:08Z)
• ContraFix: Agentic Vulnerability Repair via Differential Runtime Evidence and Skill Reuse [10.503895811137095]
  Large language model (LLM) agents are increasingly used for automated vulnerability repair.<n>Recent empirical results show that these agents still struggle with real-world vulnerabilities.<n>We present ContraFix, an agentic framework that couples runtime evidence with reusable repair skills.
  arXiv  Detail & Related papers  (2026-05-17T13:48:25Z)
• Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry [49.83108591873481]
  We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle.<n>We show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.
  arXiv  Detail & Related papers  (2026-05-12T02:11:54Z)
• Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study [51.717224133855886]
  Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments.<n>We analyze 17,022 skills (sampled from 170,226 on SkillsMP) using static analysis, sandbox testing, and manual inspection.<n>We identify 520 vulnerable skills with 1,708 issues and derive a taxonomy of 10 leakage patterns (4 accidental and 6 adversarial)
  arXiv  Detail & Related papers  (2026-04-03T14:50:16Z)
• SkillRouter: Skill Routing for LLM Agents at Scale [18.540967600258607]
  Given a user task, the system must identify relevant skills before downstream planning or execution.<n>Existing agent stacks often rely on progressive disclosure, exposing only skill names and descriptions while hiding the full implementation body.<n>We present Skill, a compact 1.2B full-text retrieve-and-rerank pipeline.
  arXiv  Detail & Related papers  (2026-03-23T18:23:59Z)

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.