Types API Reference

Types API Reference

Type definitions for pydantic-ai-skills.

Overview

The package uses dataclasses for type-safe skill representation:

• Skill - Complete skill with metadata, resources, and scripts
• SkillResource - Resource file (content) or callable resource within a skill
• SkillScript - Executable script (file or callable function) within a skill
• SkillWrapper - Generic decorator return type for @toolset.skill() supporting attachment of resources and scripts

!!! info "File-Based vs Programmatic" These types support both file-based skills (loaded from directories) and programmatic skills (created in Python code). For file-based skills, see Creating Skills. For programmatic skills, see Programmatic Skills. For advanced patterns, see Advanced Features.

Skill Class

::: pydantic_ai_skills.types.Skill options: show_source: true heading_level: 3

Key Attributes

Methods

SkillResource Class

::: pydantic_ai_skills.types.SkillResource options: show_source: true heading_level: 3

Key Attributes

File-Based Variants

FileSkillResource - Loads resource content from file:

• Auto-detects file type by extension
• Supports: .md, .json, .yaml, .yml, .csv, .xml, .txt
• JSON/YAML files are parsed; others returned as text
• Automatically validates path traversal safety

SkillScript Class

::: pydantic_ai_skills.types.SkillScript options: show_source: true heading_level: 3

Key Attributes

File-Based Variants

LocalSkillScriptExecutor - Executes file-based scripts:

• Runs Python scripts via subprocess
• Converts dict arguments to CLI flags: {"query": "test"} → --query test
• Combines stdout/stderr
• Default timeout: 30 seconds
• Execution directory: script's parent folder

CallableSkillScriptExecutor - Executes callable functions:

• Runs Python functions directly
• Passes dict arguments as function kwargs
• Supports async functions
• Receives RunContext for dependency access

SkillWrapper[T] Class

Generic type returned by @toolset.skill() decorator that enables type-safe dependency injection and attachment of resources/scripts.

Generic Parameter: T - The dependency type available in RunContext[T]

Decorators

@skill.resource
def my_resource() -> str:
    """Attach a callable resource."""
    return "..."

@skill.resource
async def context_resource(ctx: RunContext[MyDeps]) -> str:
    """Resource with access to dependencies."""
    return str(ctx.deps)

@skill.script
def my_script() -> str:
    """Attach a callable script."""
    return "..."

@skill.script
async def context_script(ctx: RunContext[MyDeps], param: str) -> str:
    """Script with dependencies and parameters."""
    return f"Executed with {param}"

Utility Functions

normalize_skill_name()

def normalize_skill_name(name: str) -> str:
    """Convert a name to valid skill name format.

    Rules:
    - Convert underscores to hyphens
    - Convert to lowercase
    - Ensure matches ^[a-z0-9]+(-[a-z0-9]+)*$

    Args:
        name: Input string (e.g., "MySkillName", "my_skill_name")

    Returns:
        Normalized skill name (e.g., "my-skill-name")
    """

Type Structures

Skill Structure

Skill
├── name: str                              # Unique identifier (lowercase, hyphens only)
├── description: str                       # Brief description (max 1024 chars)
├── content: str                           # Main instructions (markdown)
├── resources: list[SkillResource] | None  # Additional resources
├── scripts: list[SkillScript] | None      # Executable scripts
├── uri: str | None                        # Base path (file-based: set, programmatic: None)
└── metadata: dict[str, Any] | None        # Custom fields (version, author, license, etc.)

SkillResource Structure

SkillResource
├── name: str                      # Resource identifier
├── description: str | None        # Optional description
├── content: str | None            # Static content
├── function: Callable | None      # Callable (for dynamic resources)
├── takes_ctx: bool                # Requires RunContext
├── function_schema: FunctionSchema| None
└── uri: str | None                # File path (file-based only)

SkillScript Structure

SkillScript
├── name: str                      # Script identifier
├── description: str | None        # Optional description
├── function: Callable | None      # Callable (for programmatic)
├── takes_ctx: bool                # Requires RunContext
├── function_schema: FunctionSchema| None
├── uri: str | None                # File path (file-based only)
└── skill_name: str | None         # Parent skill (file-based only)

Common Metadata Fields

When creating skills, these metadata fields are commonly used:

Usage Examples

Creating Programmatic Skills

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

# Create a skill with static resources
my_skill = Skill(
    name='my-skill',
    description='A programmatic skill',
    content='Instructions for using this skill...',
    resources=[
        SkillResource(
            name='reference',
            content='## Reference\n\nStatic documentation here...'
        )
    ]
)

# Add dynamic resources
@my_skill.resource
def get_info() -> str:
    """Get dynamic information."""
    return "Dynamic content generated at runtime"

@my_skill.resource
async def get_data(ctx: RunContext[MyDeps]) -> str:
    """Get data from dependencies."""
    return await ctx.deps.fetch_data()

# Add scripts
@my_skill.script
async def process(ctx: RunContext[MyDeps], query: str) -> str:
    """Process a query.

    Args:
        query: The query to process
    """
    result = await ctx.deps.process(query)
    return f"Processed: {result}"

Working with File-Based Skills

from pydantic_ai_skills import SkillsToolset

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

# Access skills
for name, skill in toolset.skills.items():
    print(f"\nSkill: {name}")
    print(f"  Description: {skill.description}")

    if skill.uri:  # File-based skill
        print(f"  URI: {skill.uri}")

    # Access metadata
    if skill.metadata and "version" in skill.metadata:
        print(f"  Version: {skill.metadata['version']}")

    # List resources
    if skill.resources:
        print(f"  Resources:")
        for resource in skill.resources:
            print(f"    - {resource.name}")

    # List scripts
    if skill.scripts:
        print(f"  Scripts:")
        for script in skill.scripts:
            print(f"    - {script.name}")

Mixing Programmatic and File-Based Skills

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

# Create programmatic skill
custom_skill = Skill(
    name='custom-skill',
    description='Programmatic skill',
    content='Custom instructions...'
)

@custom_skill.script
async def custom_action(ctx: RunContext[MyDeps]) -> str:
    """Perform custom action."""
    return 'Action completed'

# Combine with file-based skills
toolset = SkillsToolset(
    directories=["./skills"],  # File-based skills
    skills=[custom_skill]      # Programmatic skills
)

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

Type Structures

Skill Structure

Skill
├── name: str                    # Unique skill identifier
├── description: str             # Brief description
├── content: str                 # Main instructions (markdown)
├── resources: list[SkillResource]  # Additional resources
├── scripts: list[SkillScript]   # Executable scripts
├── uri: str | None             # Base URI (file-based only)
└── metadata: dict[str, Any] | None  # Additional metadata

SkillResource Structure

SkillResource
├── name: str                    # Resource identifier
├── description: str | None     # Optional description
├── content: str | None         # Static content (for file-based or inline)
├── function: Callable | None   # Callable function (programmatic)
├── takes_ctx: bool             # Whether function takes RunContext
├── function_schema: FunctionSchema | None  # Schema for callable
└── uri: str | None             # File URI (file-based only)

SkillScript Structure

SkillScript
├── name: str                    # Script identifier
├── description: str | None     # Optional description
├── function: Callable | None   # Callable function (programmatic)
├── takes_ctx: bool             # Whether function takes RunContext
├── function_schema: FunctionSchema | None  # Schema for callable
├── uri: str | None             # File URI (file-based only)
└── skill_name: str | None      # Parent skill name

Programmatic vs File-Based

File-Based Skills

Loaded from filesystem directories:

• uri points to file/directory location
• content is loaded from SKILL.md
• Resources loaded from additional .md files
• Scripts loaded from scripts/ directory and executed via subprocess

Programmatic Skills

Created in Python code:

• No uri (no filesystem location)
• content provided directly as string
• Resources can be static content or callable functions
• Scripts are Python functions with RunContext support
• Supports dependency injection via RunContext

See Also

• SkillsToolset - Main toolset API and initialization
• Exceptions - Exception classes and error handling
• Advanced Features - Decorator patterns and custom executors
• Creating Skills - File-based skill creation guide
• Programmatic Skills - Programmatic skill creation guide