🤖 AI Summary
Current human-AI collaborative agent systems treat skills as code that is blindly trusted without verification, necessitating human review for every irreversible action and thereby hindering scalability. This work proposes redefining skills explicitly as untrusted artifacts until verified, introducing a trust model, capability gating mechanisms, and a formally verifiable dual-condition correctness criterion that triggers human oversight only for unverified skills. We present a model- and framework-agnostic trust architecture, articulate a portable runtime specification grounded in ten design principles, and validate its efficacy through an open-source implementation. Our approach significantly enhances system scalability and sustainability without requiring model retraining or proprietary infrastructure.
📝 Abstract
Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model itself -- have moved from convenience to first-class deployment artifact. The runtime that loads them inherits the same problem package managers and operating systems have always faced: a piece of content claims a behavior; the runtime must decide whether to believe it. We argue this paper's central thesis up front: a skill is \emph{untrusted code} until it is verified, and the runtime that loads it must enforce that default rather than infer trust from a signature, a clearance, or a registry of origin. Without skill verification, a human-in-the-loop (HITL) gate must fire on every irreversible call -- which is operationally untenable and degrades into rubber-stamping at any non-trivial scale. With skill verification treated as a separate, gated process, HITL fires only for what is unverified, and the system becomes sustainable. We give a trust schema (§\ref{sec:schema}) that includes an explicit verification level on every skill manifest; a capability gate (§\ref{sec:gate}) whose HITL policy is a function of that verification level; a \emph{biconditional} correctness criterion (§\ref{sec:biconditional}) that any candidate verification procedure must satisfy on an adversarial-ensemble exercise (§\ref{sec:eval}); and a portable runtime profile (§\ref{sec:guidelines}) with ten normative guidelines abstracted from a working open-source reference implementation \cite{metere2026enclawed}. The contribution is harness- and model-agnostic; nothing here requires retraining, fine-tuning, or proprietary infrastructure.