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原文摘要

Agent skills package reusable workflows and task-specific procedures into deployable natural-language artifacts for LLM agents. Their scale and readability make them valuable but vulnerable to unauthorized redistribution and repackaging. Existing prompt copyright auditing methods struggle with long skills and introduce token-level perturbations that compromise readability. We propose SkillCODER for copyright auditing and buyer attribution in a black-box, content-only setting. During embedding, SkillCODER parses a skill into a section-aware intermediate representation, SkillIR, to identify structural anchors. It distributes conditional instructions across these anchors to form an Artifact-Gated Capsule, designed to activate under verification queries and remain dormant otherwise. Controlled-Vocabulary Error-Correcting Codes encode buyer-specific fingerprints through controlled lexical choices within the capsule. During verification, the defender issues paired positive and negative probes, extracts capsule evidence, and recovers ownership and buyer identity through error-correcting decoding. Across three domains, six models, and three agent frameworks, SkillCODER achieves 99.2% owner verification and 99.3% buyer Top-1 attribution accuracy while preserving task fidelity, substantially outperforming prompt-level baselines. Embedded signals remain robust under paraphrase, compression, clause deletion, and reorganization. Under five adaptive attacks, including a method-aware LLM sanitizer and multi-buyer collusion, ownership evidence remains reliably detectable while buyer attribution degrades gracefully.