Skills¶

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)

Text Only

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

SKILL.md Format¶

Markdown

---
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¶

Field	Required	Description
`name`	Yes	Unique skill identifier
`description`	Yes	Brief description (shown in list)
`version`	No	Semantic version (default: "1.0.0")
`tags`	No	List of tags for categorization
`author`	No	Skill author

Progressive Disclosure¶

Skills use progressive disclosure to optimize token usage:

1. Discovery (Low Cost)¶

Only YAML frontmatter is loaded:

Python

# 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:

Python

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

3. Resources (As Needed)¶

Additional files accessed individually:

Python

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

Using Skills¶

Enable Skills¶

Python

from pydantic_deep import create_deep_agent

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

Available Tools¶

Tool	Description
`list_skills`	List all available skills
`load_skill`	Load full instructions for a skill
`read_skill_resource`	Read a resource file from a skill

Agent Workflow¶

The agent typically:

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

Creating Skills¶

Step 1: Create Directory¶

Bash

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

Step 2: Write SKILL.md¶

Markdown

---
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

Text Only

### 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¶

Python

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¶

To pass skills directly (without filesystem discovery), use a SkillsToolset:

Python

from pydantic_deep.toolsets.skills import Skill, SkillsToolset

skill = Skill(name="code-review", description="Review code for quality", content="...")
agent = create_deep_agent(
    toolsets=[SkillsToolset(skills=[skill])],
    include_skills=False,  # avoid duplicate skills toolset
)

Example: Code Review Skill¶

SKILL.md¶

Markdown

---
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]

Text Only

### 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:

Markdown

# 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:

Markdown

## 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,))

Text Only

### 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:

Python

# 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:

Factor	Impact
Number of skill directories	Linear scan time
`recursive: True`	Deeper directory traversal
Number of skills	More frontmatter parsing

Optimization tips:

Limit directories: Only include directories with actual skills
Use recursive: False when skills are in known locations
Pre-load skills: Pass Skill objects via SkillsToolset to avoid discovery

Python

# Fast: Pre-loaded skills via toolset (no disk scan)
agent = create_deep_agent(
    toolsets=[SkillsToolset(skills=[my_skill])],
    include_skills=False,
)

# 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:

Python

# 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:

Python

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

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

Skills with Backends¶

By default, skill_directories accepts local filesystem paths (strings or dicts). For non-local backends (in-memory, Docker, remote storage), use [BackendSkillsDirectory][pydantic_deep.toolsets.skills.backend.BackendSkillsDirectory] which discovers skills via the backend's file operations.

StateBackend (In-Memory)¶

Useful for testing or ephemeral sessions. Write skill files to the backend first, then point BackendSkillsDirectory at them:

Python

from pydantic_ai_backends import StateBackend
from pydantic_deep import create_deep_agent
from pydantic_deep.toolsets.skills.backend import BackendSkillsDirectory

backend = StateBackend()

# Write skill files into the in-memory backend
backend.write("/skills/code-review/SKILL.md", """\
---
name: code-review
description: Review Python code for quality and security
---

# Code Review Skill

When reviewing code, follow these guidelines...
""")

# Optionally add resources
backend.write("/skills/code-review/checklist.md", "# Review Checklist\n...")

# Discover skills from the backend
agent = create_deep_agent(
    skill_directories=[BackendSkillsDirectory(backend=backend, path="/skills")],
    backend=backend,
)

LocalBackend¶

With LocalBackend, skills are read through the backend abstraction layer instead of direct filesystem access. This ensures consistent path resolution:

Python

from pydantic_ai_backends import LocalBackend
from pydantic_deep import create_deep_agent
from pydantic_deep.toolsets.skills.backend import BackendSkillsDirectory

backend = LocalBackend(root_dir="/home/user/project")

agent = create_deep_agent(
    skill_directories=[BackendSkillsDirectory(backend=backend, path="/skills")],
    backend=backend,
)

DockerSandbox¶

Inside a Docker sandbox, BackendSkillsDirectory automatically enables script execution via SandboxProtocol.execute(). Skill scripts (.py files) run inside the container:

Python

from pydantic_ai_backends import DockerSandbox
from pydantic_deep import create_deep_agent
from pydantic_deep.toolsets.skills.backend import BackendSkillsDirectory

sandbox = DockerSandbox(runtime="python-minimal")

# Upload skills into the sandbox
sandbox.write("/skills/deploy/SKILL.md", skill_content)
sandbox.write("/skills/deploy/scripts/validate.py", script_content)

agent = create_deep_agent(
    skill_directories=[
        BackendSkillsDirectory(
            backend=sandbox,
            path="/skills",
            script_timeout=60,  # seconds
        ),
    ],
    backend=sandbox,
)

Script execution requires SandboxProtocol

Skill scripts (.py files in skill directories) are only discovered when the backend implements SandboxProtocol (e.g., DockerSandbox, LocalBackend with execute). With StateBackend, only resources (.md, .json, .yaml, etc.) are available.

BackendSkillsDirectory Options¶

Parameter	Type	Default	Description
`backend`	`BackendProtocol`	required	Backend to discover skills from
`path`	`str`	`"/skills"`	Base path to search for skills
`validate`	`bool`	`True`	Validate skill structure on discovery
`max_depth`	`int \\| None`	`3`	Maximum directory depth (`None` for unlimited)
`script_timeout`	`int`	`30`	Timeout for script execution in seconds

Mixing Local and Backend Directories¶

You can combine local paths and BackendSkillsDirectory in the same agent:

Python

agent = create_deep_agent(
    skill_directories=[
        "~/.pydantic-deep/skills",  # Local filesystem
        BackendSkillsDirectory(backend=sandbox, path="/skills"),  # Backend
    ],
    backend=sandbox,
)

Next Steps¶

Skills Example - Complete working example
API Reference - SkillsToolset API