2586 Toolset 53992f79

SkillsToolset API Reference

::: pydantic_ai_skills.toolset.SkillsToolset options: members: - init - get_instructions - get_skill - skills show_source: true heading_level: 2

Constructor Parameters

The SkillsToolset.__init__() accepts the following parameters:

Properties

Methods

Usage Examples

Initialize with File-Based Skills

from pydantic_ai_skills import SkillsToolset

# Basic initialization - defaults to ./skills directory
toolset = SkillsToolset()

# Explicit single directory
toolset = SkillsToolset(directories=["./skills"])

# Multiple directories
toolset = SkillsToolset(
    directories=["./skills", "./shared", "./custom"],
    validate=True,
    max_depth=3,
    id="my-skills"
)

# Using SkillsDirectory instances directly
from pydantic_ai.toolsets.skills import SkillsDirectory

skills_dir = SkillsDirectory(
    path="./skills",
    validate=True,
    max_depth=3
)
toolset = SkillsToolset(directories=[skills_dir])

Initialize with Programmatic Skills

from pydantic_ai import RunContext
from pydantic_ai.toolsets.skills import Skill, SkillsToolset

# Create a simple programmatic skill
my_skill = Skill(
    name='custom-skill',
    description='Custom programmatic skill',
    content='Instructions for this skill...'
)

# Initialize toolset with programmatic skill
toolset = SkillsToolset(skills=[my_skill])

# Use the @skill() decorator to define skills inline
skills = SkillsToolset()

@skills.skill()
def data_analyzer() -> str:
    """Analyze data from various sources."""
    return "Provide data analysis capabilities..."

@data_analyzer.resource
def get_schema() -> str:
    """Get available schema information."""
    return "## Schema\n\nAvailable columns..."

@data_analyzer.script
async def analyze(ctx: RunContext[MyDeps], query: str) -> str:
    """Run analysis query."""
    return await ctx.deps.database.execute(query)

Mix File-Based and Programmatic Skills

from pydantic_ai.toolsets.skills import Skill, SkillsToolset

# Create programmatic skills
programmatic_skill = Skill(
    name='runtime-skill',
    description='Created at runtime',
    content='Dynamic skill content...'
)

# Combine both types in a single toolset
toolset = SkillsToolset(
    directories=["./skills"],        # File-based skills from directory
    skills=[programmatic_skill],     # Programmatic skills
    max_depth=3                       # Limit directory discovery depth
)

# Programmatic skills can also be added via decorator
@toolset.skill()
def extra_skill() -> str:
    return "Additional dynamically-defined skill..."

print(f"Total skills loaded: {len(toolset.skills)}")

Custom Instruction Template

from pydantic_ai import Agent
from pydantic_ai.toolsets.skills import SkillsToolset

custom_instructions = """\
You have specialized skills available for specific domains.

Each skill includes instructions, resources, and executable scripts.

Available skills:
{skills_list}

Use `load_skill` to explore any skill that's relevant to the user's request.
"""

toolset = SkillsToolset(
    directories=["./skills"],
    instruction_template=custom_instructions
)

agent = Agent(
    model='openai:gpt-4o',
    toolsets=[toolset]
)

Use @skill() Decorator

from pydantic_ai.toolsets.skills import SkillsToolset

skills = SkillsToolset(directories=["./skills"])

@skills.skill(
    name='custom-analyzer',  # Override function name
    license='Apache-2.0',
    compatibility='Python 3.10+',
    metadata={'version': '1.0.0', 'author': 'my-team'}
)
def data_analyzer() -> str:
    """Analyze data from various sources."""
    return """
# Data Analysis Skill

Use this skill to analyze datasets and generate insights.

## Instructions

Load the full skill with `load_skill` to see available resources and scripts.
"""

# Now 'data-analyzer' skill is registered and available to agents

Get Skills Instructions for Agent

from pydantic_ai import Agent, RunContext
from pydantic_ai_skills import SkillsToolset

toolset = SkillsToolset(directories=["./skills"])

agent = Agent(
    model='openai:gpt-4o',
    instructions="You are a helpful assistant.",
    toolsets=[toolset]
)

@agent.instructions
async def add_skills(ctx: RunContext) -> str | None:
    """Inject skills instructions into agent context."""
    return toolset.get_instructions(ctx)

# The agent will receive skill metadata in system prompt
result = agent.run_sync('Analyze the quarterly data')

The instructions include:

• List of available skills with descriptions
• How to use the four skill tools
• Best practices for progressive disclosure

Access Skills

# Get all skills
all_skills = toolset.skills

# Get specific skill
skill = toolset.get_skill("arxiv-search")

print(f"Name: {skill.name}")
print(f"Description: {skill.metadata.description}")
print(f"Scripts: {[s.name for s in skill.scripts]}")

Tools Provided

The SkillsToolset automatically registers four tools with agents:

list_skills()

Lists all available skills with descriptions.

Returns: Formatted markdown string

Example:

# Available Skills

## arxiv-search

Search arXiv for research papers (scripts: arxiv_search)

## web-research

Structured approach to web research

load_skill(skill_name: str)

Loads full instructions for a specific skill.

Parameters:

• skill_name (str): Name of the skill to load

Returns: Full skill content including metadata and instructions

read_skill_resource(skill_name: str, resource_name: str)

Reads a resource file from a skill.

Parameters:

• skill_name (str): Name of the skill
• resource_name (str): Resource filename (e.g., "REFERENCE.md")

Returns: Resource file content

run_skill_script(skill_name: str, script_name: str, args: dict[str, Any] | None = None)

Executes a skill script with optional arguments.

Parameters:

• skill_name (str): Name of the skill
• script_name (str): Name of the script within the skill
• args (dict[str, Any], optional): Dictionary of arguments to pass to the script
  • For file-based scripts: Converted to command-line flags (e.g., {"query": "test"} → --query test)
  • For callable scripts: Passed as function arguments

Returns: String output from script execution

Raises:

• SkillNotFoundError: If the skill doesn't exist
• SkillScriptNotFoundError: If the script doesn't exist in the skill
• SkillScriptExecutionError: If script execution fails or times out

Example:

# Agent calls the tool
agent.run_sync('Run the arxiv search script with query "machine learning"')

# Internally calls:
# result = await toolset.run_skill_script(
#     'arxiv-search',
#     'arxiv_search',
#     {'query': 'machine learning'}
# )

Decorator: @toolset.skill()

The @toolset.skill() decorator enables defining skills directly on the toolset instance.

Signature:

def skill(
    func: Callable[[], str] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    license: str | None = None,
    compatibility: str | None = None,
    metadata: dict[str, Any] | None = None,
    resources: list[SkillResource] | None = None,
    scripts: list[SkillScript] | None = None,
) -> SkillWrapper[Any]

Parameters:

Returns: SkillWrapper[Any] - Decorated skill that supports @skill.resource and @skill.script decorators

Example:

from pydantic_ai.toolsets.skills import SkillsToolset

skills = SkillsToolset()

@skills.skill(
    name='data-analyzer',
    license='MIT',
    compatibility='Python 3.10+',
    metadata={'version': '1.0.0', 'author': 'data-team'}
)
def my_analyzer() -> str:
    """Analyze and process data."""
    return "# Data Analysis Skill\n\nUse for data analysis tasks."

# Extended with resources and scripts
@my_analyzer.resource
def get_schema() -> str:
    return "## Schema\n\nDatabase schema information..."

@my_analyzer.script
async def analyze_data(query: str) -> str:
    return f"Analyzed: {query}"

See Advanced Features for detailed decorator documentation.

See Also

• Advanced Features - Skill decorators, custom templates, dependency injection
• Types Reference - Type definitions and data structures
• Exceptions Reference - Exception classes