Skills

Learn how to extend your deep agent’s capabilities with skills

Skills are reusable agent capabilities that provide specialized workflows and domain knowledge. You can use Agent Skills to provide your deep agent with new capabilities and expertise. Deep agent skills follow the Agent Skills standard.

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)

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.

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.

Usage

Pass the skills directory when creating your deep agent:

StateBackend

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"}},
)

StoreBackend

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from langgraph.store.memory import InMemoryStore


store = InMemoryStore()

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')

store.put(
    namespace=("filesystem",),
    key="/skills/langgraph-docs/SKILL.md",
    value=skill_content
)

agent = create_deep_agent(
    backend=(lambda rt: StoreBackend(rt)),
    store=store,
    skills=["./skills/"]
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ]
    },
    config={"configurable": {"thread_id": "12345"}},
)

FilesystemBackend

from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
from deepagents.backends.filesystem import FilesystemBackend

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    skills=["/Users/user/{project}/skills/"],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True    # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ]
    },
    config={"configurable": {"thread_id": "12345"}},
)

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).

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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