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You can use Agent Skills to provide your deep agent with new capabilities and expertise.

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What are skills

Skills are a directory of folders, where each folder has one or more files that contain context the agent can use:
  • a SKILL.md file containing instructions and metadata about the skill
  • additional scripts (optional)
  • additional reference info, such as docs (optional)
  • additional assets, such as templates and other resources (optional)

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How do skills work

When you create a deep agent, you can pass in a list of directories containing skills. As the agent starts, it reads through the frontmatter of each SKILL.md file. When the agent receives a prompt, the agent checks if it can use any skills while fulfilling the prompt. If it finds a matching prompt, it then reviews the rest of the skill files. This pattern of only reviewing the skill information when needed is called progressive disclosure.

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Examples

You might have a skills folder that contains a skill to use a docs site in a certain way, as well as another skill to search the arXiv preprint repository of research papers: 
    skills/
    ├── langgraph-docs
    │   └── SKILL.md
    └── arxiv_search
        ├── SKILL.md
        └── arxiv_search.ts # code for searchign arXiv
The SKILL.md file always follows the same pattern, starting with metadata in the frontmatter and followed by the instructions for the skill. The following example shows a skill that gives instructions on how to provide relevant langgraph docs when prompted: 
---
name: langgraph-docs
description: Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.
---

# langgraph-docs

## Overview

This skill explains how to access LangGraph Python documentation to help answer questions and guide implementation.

## Instructions

### 1. Fetch the Documentation Index

Use the fetch_url tool to read the following URL:
https://docs.langchain.com/llms.txt

This provides a structured list of all available documentation with descriptions.

### 2. Select Relevant Documentation

Based on the question, identify 2-4 most relevant documentation URLs from the index. Prioritize:

- Specific how-to guides for implementation questions
- Core concept pages for understanding questions
- Tutorials for end-to-end examples
- Reference docs for API details

### 3. Fetch Selected Documentation

Use the fetch_url tool to read the selected documentation URLs.

### 4. Provide Accurate Guidance

After reading the documentation, complete the user's request.
For more example skills, see Deep Agent example skills.

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Usage

Pass the skills directory when creating your deep agent: 
from urllib.request import urlopen
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md"
with urlopen(skill_url) as response:
    skill_content = response.read().decode('utf-8')

skills_files = {
    "/skills/langgraph-docs/SKILL.md": skill_content
}

agent = create_deep_agent(
    skills=["./skills/"],
    checkpointer=checkpointer,
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ],
        # Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
        "files": skills_files
    },
    config={"configurable": {"thread_id": "12345"}},
)
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skills
list[str]
List of skill source paths. Paths must be specified using forward slashes and are relative to the backend’s root.
  • When using StateBackend (default), provide skill files with invoke(files={...}).
  • With FilesystemBackend, skills are loaded from disk relative to the backend’s root_dir.
Later sources override earlier ones for skills with the same name (last one wins).

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When to use skills and tools

These are a few general guidelines for using tools and skills:
  • Use skills when there is a lot of context to reduce the number of tokens in the system prompt.
  • Use skills to bundle capabilities together into larger actions and provide additional context beyond single tool descriptions.
  • Use tools if the agent does not have access to the file system.
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