Skills for AI agents: comparing four incompatible approaches

Description and use

The article outlines four incompatible skill approaches for AI agents and offers a comparison matrix and a static audit script to evaluate provenance, versioning, permissions, and executable content.

Problem

Choosing a skill framework for an AI agent is ambiguous because the term ‘skill’ can refer to four distinct architectures—instruction, trained, local package, or registry—each with different trust boundaries, update paths, and risks.

Outcome

Teams can systematically compare skill approaches, identify risks, and choose the most appropriate architecture. The audit script demonstrates how to catch flaws in a demo skill, ensuring safer deployment.

Workflow

• 1. Identify the skill type (instruction, trained, local package, registry).
• 2. Gather provenance information (Git commit, training run, registry publisher).
• 3. Verify versioning (file diff, artifact hash, semver, pinned digest).
• 4. Assess permissions (agent tools, manifest declarations, publisher policy).
• 5. Check for executable content (scripts, binaries, remote execution).
• 6. Run the static audit script on the skill artifacts.
• 7. Review audit results and decide on the appropriate skill approach.

Evidence

"Add a skill to the agent" can mean four different things: write a Markdown instruction, retrain the model, drop a folder of scripts into the repository, or install something by name from a remote catalog.

Source

The four audit criteria are deliberately boring, because boring criteria are the ones that catch incidents: Provenance, Versioning, Permissions, Executable content.

Source

We will create a small local-package skill called changelog-summarizer. It has an instruction file, a manifest and one shell script.

Source