An agent skill is arbitrary code that runs with the agent's own authority, installed from a registry with no review gate. This paper gives the threat model a formal treatment, then builds static analysis, provenance verification and SBOM generation across 22 agent frameworks so a skill can be judged before it executes.
Our summary of the paper. The full abstract is on arXiv. Preprint, not peer reviewed.
Authors
Varun Pratap Bhardwaj
Cite this paper
@misc{bhardwaj2026skillfortify,
title = {Formal Analysis and Supply Chain Security for Agentic AI Skills},
author = {Varun Pratap Bhardwaj},
year = {2026},
eprint = {2603.00195},
archivePrefix= {arXiv},
primaryClass = {cs.CR},
url = {https://arxiv.org/abs/2603.00195}
}
I maintain 7 AI products, 4 websites, and active research for $24/month — under $1/day. Here's the exact four-CLI architecture with real benchmarks, free models, and the Qualixar tooling stack.
Most AI agent frameworks skip output quality entirely. We built a 2-round adversarial judge pipeline with multi-model consensus, anti-fabrication verification, and configurable profiles — and tested the same principle by having 7 independent AI auditors evaluate our own codebase.