Agent Skills

Agent Skills are reusable packages of instructions, metadata, reference materials, and optional scripts that give an AI agent specialized expertise for a particular task. Instead of placing every procedure in a long system prompt, developers can package a focused capability—such as inspecting products, analyzing spreadsheets, or following an organization’s deployment checklist—and let the agent load it only when relevant.

Skills help a general-purpose AI agent behave more like a domain specialist while keeping knowledge modular, maintainable, and portable. “Claude Skills” are one product implementation of this broader idea; the term Agent Skills can also refer to the interoperable format described by the open Agent Skills overview.

How Agent Skills Work#

Under the Agent Skills specification, a skill is typically a directory containing a required SKILL.md file. This file begins with YAML metadata, including a name and a description explaining what the skill does and when the agent should activate it. Its Markdown body provides procedures, examples, constraints, and edge-case guidance. Optional directories can contain executable scripts, reference documents, schemas, or templates.

Skills use progressive disclosure, meaning information enters the agent’s context window in stages:

1. The agent initially sees short names and descriptions for available skills.
2. When a request matches a description, it loads the relevant SKILL.md.
3. It reads supporting resources or runs bundled scripts only when needed.

This reduces unnecessary token usage and lets an agent access many specialized capabilities without loading every instruction at startup. The Claude Agent Skills documentation and Microsoft Agent Skills documentation provide examples of this loading pattern in agent platforms.

Agent Skills vs. Related Concepts#

Agent Skills organize knowledge and procedures, but they do not replace the agent, its tools, or the system coordinating its actions.

• Tools and function calling: A tool performs an individual operation, such as querying an API or running inference. A skill explains when and how to use one or more tools together, including validation rules and domain context.

• Agentic workflows: A skill lets the agent adaptively interpret instructions. A workflow defines a more explicit execution path, making it preferable for long-running processes, checkpoints, approvals, and actions that must occur in a fixed order.

• Model Context Protocol: MCP standardizes how agents discover and invoke external resources and tools. The official MCP tools specification defines tool interfaces, while a skill packages procedural expertise that may direct an agent to use those interfaces.

A prompt is usually written for one conversation. A skill is intended to be reused, versioned, tested, and shared across compatible environments.

Applications in Computer Vision#

Agent Skills are particularly useful when visual perception must be combined with business rules and follow-up actions.

Manufacturing inspection: A quality-control skill can instruct an agent to capture an image, run object detection, compare detections with acceptance criteria, save evidence, and request human review when confidence is low. The vision model supplies observations, while the skill provides the factory-specific inspection procedure. This supports repeatable defect detection workflows without embedding every policy in the agent’s base prompt.

Retail inventory auditing: A shelf-audit skill can guide an agent to process store images, count specified products, compare results with inventory records, and prepare a replenishment recommendation. Teams can use Ultralytics Platform to annotate shelf data, train a custom model, deploy it, and monitor the visual tool that the skill calls.

In both cases, skills connect perception to operational knowledge. They do not improve the underlying model’s visual accuracy by themselves; they make its outputs easier to apply consistently.

Example Vision Script for a Skill#

A skill can bundle a deterministic script for visual inspection. The following documented Ultralytics YOLO prediction workflow could be stored in a skill’s scripts/ directory:

from pathlib import Path

from ultralytics import YOLO

image_url = "https://ultralytics.com/images/bus.jpg"
output_path = Path("inspection_result.jpg")

# Run the skill's visual perception step
model = YOLO("yolo26n.pt")
results = model(image_url)

result = results[0]
result.save(filename=str(output_path))

print(output_path)

Here, YOLO26 provides the visual capability, while SKILL.md would explain when to run the script, how to interpret its output, and what action should follow.

Design and Security Considerations#

Effective skills should have narrow responsibilities, clear activation descriptions, concise instructions, realistic examples, and documented dependencies. The official skill authoring best practices recommend matching instruction detail to task risk: flexible guidance can suit open-ended analysis, while fragile operations need exact scripts and tighter constraints.

Third-party skills should be treated like installed software. Review every instruction, script, dependency, network call, and file operation before granting access. Apply sandboxing, least-privilege permissions, resource limits, logging, and human approval for consequential actions. These controls help address risks described by the OWASP prompt injection guidance and OWASP excessive agency guidance. Teams can also use the NIST AI Risk Management Framework Playbook to integrate skill evaluation, monitoring, and governance into broader AI risk management.