MCPInstall

config-skills

Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/llama-farm/llamafarm --skill config-skills
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads config-skills automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Config Skills for LlamaFarm

Specialized patterns and best practices for the LlamaFarm configuration module (config/).

Module Overview

The config module provides YAML/TOML/JSON configuration loading with JSONSchema validation:

FilePurpose
datamodel.pyGenerated Pydantic v2 models from JSONSchema
schema.yamlSource JSONSchema with $ref references
compile_schema.pyDereferences $ref to create schema.deref.yaml
generate_types.pyGenerates Python types via datamodel-codegen
validators.pyCustom validators beyond JSONSchema capabilities
helpers/loader.pyConfig loading, saving, and format detection
helpers/generator.pyTemplate-based config generation

Links to Shared Skills

This module follows Python conventions from the shared skills:

TopicLinkKey Relevance
Patternspython-skills/patterns.mdPydantic v2, dataclasses
Typingpython-skills/typing.mdType hints, constrained types
Testingpython-skills/testing.mdPytest fixtures, temp files
Errorspython-skills/error-handling.mdCustom exceptions
Securitypython-skills/security.mdPath traversal prevention

Framework-Specific Checklists

ChecklistDescription
pydantic.mdPydantic v2 configuration patterns, nested models, constraints
jsonschema.mdJSONSchema generation, dereferencing, validation

Tech Stack

  • Python: 3.11+
  • Pydantic: v2 with ConfigDict, Field, constrained types
  • JSONSchema: Draft-07 with $ref dereferencing via jsonref
  • YAML: ruamel.yaml for comment-preserving read/write
  • Code Generation: datamodel-codegen for schema-to-Pydantic

Key Patterns

Generated Pydantic Models

The datamodel.py file is auto-generated from JSONSchema:

# Generated by datamodel-codegen from schema.deref.yaml
from pydantic import BaseModel, ConfigDict, Field, conint, constr

class Database(BaseModel):
    model_config = ConfigDict(extra="forbid")
    name: constr(pattern=r"^[a-z][a-z0-9_]*$", min_length=1, max_length=50)
    type: Type
    config: dict[str, Any] | None = Field(None, description="Database-specific configuration")

Custom Validators for Cross-Field Constraints

JSONSchema draft-07 cannot express all constraints. Custom validators extend validation:

def validate_llamafarm_config(config_dict: dict[str, Any]) -> None:
    """Validate constraints beyond JSONSchema (uniqueness, references)."""
    # Check for duplicate prompt names
    prompt_names = [p.get("name") for p in config_dict.get("prompts", [])]
    duplicates = [name for name in prompt_names if prompt_names.count(name) > 1]
    if duplicates:
        raise ValueError(f"Duplicate prompt set names: {', '.join(set(duplicates))}")

Comment-Preserving YAML with ruamel.yaml

Configuration files preserve user comments when modified:

from ruamel.yaml import YAML
from ruamel.yaml.comments import CommentedMap

def _get_ruamel_yaml() -> YAML:
    yaml_instance = YAML()
    yaml_instance.preserve_quotes = True
    yaml_instance.indent(mapping=2, sequence=4, offset=2)
    return yaml_instance

Directory Structure

config/
├── pyproject.toml       # UV-managed dependencies
├── schema.yaml          # Source JSONSchema with $ref
├── schema.deref.yaml    # Dereferenced schema (generated)
├── datamodel.py         # Pydantic models (generated)
├── compile_schema.py    # Schema compilation script
├── generate_types.py    # Type generation script
├── validators.py        # Custom validation beyond JSONSchema
├── validate_config.py   # CLI validation wrapper
├── __init__.py          # Public API exports
├── helpers/
│   ├── loader.py        # Config loading/saving
│   └── generator.py     # Template-based generation
├── templates/
│   └── default.yaml     # Default config template
└── tests/
    ├── conftest.py      # Shared fixtures
    └── test_*.py        # Test modules

Workflow: Schema Changes

When modifying the configuration schema:

  1. Edit schema.yaml (or referenced schemas like ../rag/schema.yaml)
  2. Run nx run generate-types to compile and generate types
  3. Update validators.py if new cross-field constraints are needed
  4. Test with uv run pytest config/tests/

Common Commands

# Generate types from schema
nx run generate-types

# Validate a config file
uv run python config/validate_config.py path/to/llamafarm.yaml --verbose

# Run tests
uv run pytest config/tests/ -v

# Lint and format
ruff check config/ --fix
ruff format config/

FAQ

What does the config-skills skill do?

Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.

How do I install config-skills?

Run npx skills add https://github.com/llama-farm/llamafarm --skill config-skills in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/config-skills/ (personal) or .claude/skills/config-skills/ (project).

Which AI agents can use config-skills?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is config-skills safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains config-skills?

It is published by llama-farm in llama-farm/llamafarm. It has 42 recorded installs.

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