The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills

2026-06-30

AI Summary

This study addresses the lack of a stable identity mechanism for AI agent skills that preserves semantic similarity, a limitation exacerbated by conventional hashing methods' inability to support fine-grained comparison. To overcome this, the authors propose a locality-sensitive fingerprinting approach based on multiple SimHash instances, which decomposes each skill into three components—prompt, code, and tool—and embeds them separately to generate a fixed 120-byte tripartite fingerprint. This representation enables accurate recovery of skill family identity, precise attribution of reused components, and reliable detection of independently implemented variants, even after rewriting, renaming, refactoring, or cross-language reimplementation. Experimental results demonstrate an AUC of 0.974 across 4,950 skill pairs, a 77-fold reduction in bit overhead, and exact identification of modified components in all 906 tampered samples.

Abstract

AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Governing them needs a stable notion of skill identity, yet cryptographic hashing is engineered to destroy the very similarity we need, as a one-character edit scrambles the digest. We present a compact, locality-sensitive fingerprint that embeds each component of a skill and projects it to bits with a multi-bank SimHash, giving a fixed 120-byte signature compared in constant time by Hamming distance. Our central claim is that keeping the fingerprint as a per-component triple (prompt, code, tools), rather than a single score, is what makes it useful: the triple recovers skill-family identity through paraphrase, renaming, refactoring, and controlled code translation when another component remains shared, while independent multilingual reimplementation is not recovered; it also localizes which component carries the reuse. We claim lineage, not behavioral equivalence: identity supplies the structural axis of a registry and leaves safety to behavioral verification. The fingerprint reaches an area under the ROC curve (AUC) of 0.974 (95% CI [0.956, 0.994]) over 4,950 pairwise comparisons while using 77x fewer bits than the embedding it approximates, with ranking preserved in expectation and finite-bit concentration; the per-component split turns one number into relationship classification, families, novelty, and a portable "SkillBOM" for a skill registry. On a 906-skill injection benchmark the fingerprint recognizes injected skills as tampered copies of a known base and localizes the change, but recognition is not trust: it remains, by design, an identity signal complementary to behavioral verification rather than a safety verdict.

Problem

• skill identity
• AI agents
• locality-sensitive hashing
• component decomposition
• fingerprinting

Innovation

• skill fingerprinting
• per-component identity
• SimHash
• AI agent skills
• locality-sensitive hashing