Core Concepts

Core Concepts

Understanding the key concepts behind pydantic-ai-skills will help you build better agent systems.

Skill Structure

Skills are modular packages that extend agent capabilities. You can create them in two ways:

File-Based Skills

A directory containing:

• SKILL.md - Metadata (YAML frontmatter) and instructions (required)
• scripts/ - Optional executable Python scripts
• resources/ - Optional additional documentation or data

my-skill/
├── SKILL.md # Required: Instructions and metadata
├── scripts/ # Optional: Executable scripts
│ └── my_script.py
└── resources/ # Optional: Additional files
├── reference.md
└── data.json

Programmatic Skills

Alternatively, define skills directly in Python using the Skill class with decorators for dynamic resources and scripts. This enables dependency injection and runtime configuration. See Programmatic Skills for details.

SKILL.md Format

Each SKILL.md file has YAML frontmatter (metadata) followed by Markdown (instructions).

Minimal example:

---
name: my-skill
description: A brief description of what this skill does
---

Required fields:

• name - Unique identifier (lowercase, hyphens, ≤64 chars)
• description - Brief summary (≤1024 chars, appears in listings)

Optional fields:

---
name: arxiv-search
description: Search arXiv for research papers
version: 1.0.0
author: Your Name
category: research
---

Progressive Disclosure

The toolset implements progressive disclosure - exposing information only when needed:

1. Initial: Skill names and descriptions are added to agent's instructions via @agent.instructions decorator calling get_instructions(ctx)
2. Loading: Agent calls load_skill(name) to get full instructions when needed
3. Resources: Agent calls read_skill_resource() for additional documentation
4. Execution: Agent calls run_skill_script() to execute scripts

This approach:

Skills use progressive disclosure to minimize context:

1. Discovery: Skill names/descriptions are added to instructions via get_instructions()
2. Loading: Agent calls load_skill(name) for full instructions when needed
3. Resources: Agent calls read_skill_resource() for additional files
4. Execution: Agent calls run_skill_script() to execute code

This reduces token usage, enables dynamic capability discovery, and scales to many skills. Returns: Formatted markdown with skill names and descriptions

When to use: Optional - skills are already listed in the agent's instructions via get_instructions(ctx). Use only if the agent needs to re-check available skills dynamically.

2. load_skill(name)

Lo# The Four Tools

id="skills"              # Unique identifier

)

### Key Methods

- `get_instructions(ctx)` - Get instructions text (automatically injected into agent)
- `get_skill(name)` - Get a specific skill object
- `refresh()` - Re-scan directories for skills (if using SkillsDirectory instances)

### Properties

- `skills` - Dictionary of loaded skills (`dict[str, Skill]`)

## Skill Discovery

Skills are discovered by scanning directories for `SKILL.md` files using the `SkillsDirectory` class:

```python
from pydantic_ai_skills import SkillsDirectory

skill_dir = SkillsDirectory(path="./skills", validate=True)
all_skills = skill_dir.get_skills()

for name, skill in all_skills.items():
    print(f"{name}: {skill.metadata.description}")

Type Safety

The package provides type-safe dataclasses for working with skills:

SkillMetadata

from pydantic_ai_skills import SkillMetadata

metadata = SkillMetadata(
    name="my-skill",
    description="My skill description",
    extra={"version": "1.0.0", "author": "Me"}
)

Skill

from pydantic_ai_skills import Skill

skill = Skill(
    name="my-skill",
    path=Path("./skills/my-skill"),
    metadata=metadata,
    content="# Instructions...",
    resources=[...],
    scripts=[...]
)

SkillResource

from pydantic_ai_skills import SkillResource

resource = SkillResource(
    name="reference.md",
    path=Path("./skills/my-skill/resources/reference.md"),
    content=None  # Lazy-loaded
)

SkillScript

from pydantic_ai_skills import SkillScript

script = SkillScript(
    name="my_script",
    path=Path("./skills/my-skill/scripts/my_script.py"),
    skill_name="my-skill"
)

Security

The toolset implements security measures:

• Path Validation: Scripts and resources must be within the skill directory
• No Path Traversal: Attempts to access files outside the skill directory are blocked
• Script Timeout: Scripts are killed after the configured timeout
• Safe Execution: Scripts run in a subprocess with limited privileges

Next Steps

• Creating Skills - Learn how to build skills
• Skill Patterns - Common patterns and best practices
• API Reference - Detailed API documentation