What are Skills?

Skills

!!! note "Future Migration" This implementation will be removed when skills support is added to pydantic-ai core. See pydantic-ai#3780 for progress. We will migrate to use the upstream implementation once available.

Skills are modular packages that extend agent capabilities through filesystem-based configuration. They enable progressive disclosure - only loading detailed instructions when needed.

What are Skills?

A skill is a folder containing:

• SKILL.md - Definition with YAML frontmatter and instructions
• Optional resource files (templates, scripts, documents)

~/.pydantic-deep/skills/
├── code-review/
│   ├── SKILL.md
│   └── checklist.md
├── test-generator/
│   ├── SKILL.md
│   └── templates/
│       └── pytest.py
└── documentation/
    └── SKILL.md

SKILL.md Format

---
name: code-review
description: Review Python code for quality and security
version: 1.0.0
tags:
  - code
  - review
  - python
author: your-name
---

# Code Review Skill

Detailed instructions for the agent...

## Review Process

1. Read the entire file first
2. Check for security issues
3. Review code structure
...

Frontmatter Fields

Progressive Disclosure

Skills use progressive disclosure to optimize token usage:

1. Discovery (Low Cost)

Only YAML frontmatter is loaded:

# Agent calls list_skills()
# Returns:
# - code-review: Review Python code for quality and security [code, review]
# - test-generator: Generate pytest test cases [testing, python]

2. Loading (On Demand)

Full instructions loaded when needed:

# Agent calls load_skill("code-review")
# Returns complete SKILL.md content

3. Resources (As Needed)

Additional files accessed individually:

# Agent calls read_skill_resource("code-review", "checklist.md")
# Returns specific resource file

Using Skills

Enable Skills

from pydantic_deep import create_deep_agent

agent = create_deep_agent(
    skill_directories=[
        {"path": "~/.pydantic-deep/skills", "recursive": True},
    ],
    include_skills=True,
)

Available Tools

Agent Workflow

The agent typically:

1. Receives a task ("Review this code for security issues")
2. Lists available skills to find relevant ones
3. Loads the appropriate skill instructions
4. Follows the skill's guidelines
5. Accesses resources as needed

Creating Skills

Step 1: Create Directory

mkdir -p ~/.pydantic-deep/skills/my-skill

Step 2: Write SKILL.md

---
name: api-design
description: Design RESTful APIs following best practices
version: 1.0.0
tags:
  - api
  - rest
  - design
author: your-name
---

# API Design Skill

You are an API design expert. When designing APIs, follow these principles:

## REST Conventions

- Use nouns for resources: `/users`, `/orders`
- Use HTTP methods correctly: GET, POST, PUT, DELETE
- Version your API: `/v1/users`

## Response Format

Always return JSON with consistent structure:

```json
{
  "data": {...},
  "meta": {
    "page": 1,
    "total": 100
  }
}

Error Handling

Return appropriate status codes:

• 400: Bad request
• 401: Unauthorized
• 404: Not found
• 500: Server error

### Step 3: Add Resources (Optional)

```bash
# Create a template file
cat > ~/.pydantic-deep/skills/api-design/openapi-template.yaml << 'EOF'
openapi: 3.0.0
info:
  title: API Name
  version: 1.0.0
paths: {}
EOF

Skill Discovery

From Directories

from pydantic_deep.toolsets.skills import discover_skills

skills = discover_skills([
    {"path": "~/.pydantic-deep/skills", "recursive": True},
    {"path": "./project-skills", "recursive": False},
])

for skill in skills:
    print(f"{skill['name']}: {skill['description']}")

Pre-loaded Skills

skills = [
    {
        "name": "code-review",
        "description": "Review code for quality",
        "path": "/path/to/skill",
        "tags": ["code"],
        "version": "1.0.0",
        "author": "",
        "frontmatter_loaded": True,
    }
]

agent = create_deep_agent(skills=skills)

Example: Code Review Skill

SKILL.md

---
name: code-review
description: Review Python code for quality, security, and best practices
version: 1.0.0
tags:
  - code
  - review
  - python
  - security
---

# Code Review Skill

When reviewing code, follow this checklist:

## Security

- [ ] No hardcoded secrets
- [ ] Input validation on external data
- [ ] No SQL injection vulnerabilities
- [ ] Proper error handling

## Code Quality

- [ ] Functions have single responsibility
- [ ] Descriptive variable names
- [ ] Type hints present
- [ ] Docstrings for public functions

## Output Format

```markdown
## Summary
[Brief assessment]

## Critical Issues
- [List security/major bugs]

## Improvements
- [Suggested improvements]

## Good Practices
- [Positive aspects]

### Usage

```python
agent = create_deep_agent(
    skill_directories=[{"path": "./skills"}],
)

result = await agent.run(
    "Load the code-review skill and review /src/auth.py",
    deps=deps,
)

Best Practices

1. Clear Names

Use descriptive, hyphenated names:

• ✅ code-review, test-generator, api-design
• ❌ cr, skill1, mySkill

2. Focused Skills

Each skill should do one thing well:

• ✅ code-review - Reviews code
• ❌ code-review-and-testing-and-docs - Too broad

3. Actionable Instructions

Write instructions the agent can follow:

# Good
When you find a security issue:
1. Note the file and line number
2. Describe the vulnerability
3. Suggest a fix with code example

# Bad
Be careful about security.

4. Include Examples

Show expected output format:

## Example Review

**File:** auth.py:42
**Issue:** SQL Injection
**Severity:** Critical

```python
# Bad
query = f"SELECT * FROM users WHERE id = {user_id}"

# Good
query = "SELECT * FROM users WHERE id = ?"
cursor.execute(query, (user_id,))

### 5. Version Your Skills

Update version when making changes:

```yaml
version: 1.0.0  # Initial release
version: 1.1.0  # Added new checklist items
version: 2.0.0  # Breaking changes to format

Caching and Performance

Skill Caching

Skills are cached after first load to avoid repeated file reads:

# First call reads from disk
load_skill("code-review")  # Reads SKILL.md

# Subsequent calls use cache
load_skill("code-review")  # Returns cached content (instant)

The cache persists for the lifetime of the agent instance. Each agent has its own cache.

Discovery Performance

Skill discovery (discover_skills()) scans directories on agent creation:

Optimization tips:

1. Limit directories: Only include directories with actual skills
2. Use recursive: False when skills are in known locations
3. Pre-load skills: Pass skills directly to avoid discovery

# Fast: Pre-loaded skills (no disk scan)
agent = create_deep_agent(skills=my_skills)

# Moderate: Single directory, non-recursive
agent = create_deep_agent(
    skill_directories=[{"path": "./skills", "recursive": False}]
)

# Slower: Multiple directories, recursive
agent = create_deep_agent(
    skill_directories=[
        {"path": "~/.pydantic-deep/skills", "recursive": True},
        {"path": "/shared/skills", "recursive": True},
    ]
)

Resource File Security

Resource file access is restricted to the skill's directory:

# Valid: Within skill directory
read_skill_resource("code-review", "checklist.md")
read_skill_resource("code-review", "templates/pytest.py")

# Invalid: Path traversal blocked
read_skill_resource("code-review", "../other-skill/secret.md")  # Error
read_skill_resource("code-review", "/etc/passwd")               # Error

Default Skill Directory

The default skill directory is ~/.pydantic-deep/skills:

# Uses default directory
agent = create_deep_agent(include_skills=True)

# Equivalent to:
agent = create_deep_agent(
    skill_directories=[{"path": "~/.pydantic-deep/skills", "recursive": True}]
)

Next Steps

• Skills Example - Complete working example
• API Reference - SkillsToolset API