Skip to content
Development
Skill

/config-skills

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

From plugin
llamafarm
83519 skills1 MCP
Install
$ npx -y skills add llama-farm/llamafarm --skill config-skills --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/config-skills

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

config-skills.SKILL.md
name: config-skills
description: Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.
allowed-tools: Read, Grep, Glob
user-invocable: false

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:

| File | Purpose | |------|---------| | `datamodel.py` | Generated Pydantic v2 models from JSONSchema | | `schema.yaml` | Source JSONSchema with `$ref` references | | `compile_schema.py` | Dereferences `$ref` to create `schema.deref.yaml` | | `generate_types.py` | Generates Python types via `datamodel-codegen` | | `validators.py` | Custom validators beyond JSONSchema capabilities | | `helpers/loader.py` | Config loading, saving, and format detection | | `helpers/generator.py` | Template-based config generation |

Links to Shared Skills

This module follows Python conventions from the shared skills:

| Topic | Link | Key Relevance | |-------|------|---------------| | Patterns | [python-skills/patterns.md](../python-skills/patterns.md) | Pydantic v2, dataclasses | | Typing | [python-skills/typing.md](../python-skills/typing.md) | Type hints, constrained types | | Testing | [python-skills/testing.md](../python-skills/testing.md) | Pytest fixtures, temp files | | Errors | [python-skills/error-handling.md](../python-skills/error-handling.md) | Custom exceptions | | Security | [python-skills/security.md](../python-skills/security.md) | Path traversal prevention |

Framework-Specific Checklists

| Checklist | Description | |-----------|-------------| | [pydantic.md](pydantic.md) | Pydantic v2 configuration patterns, nested models, constraints | | [jsonschema.md](jsonschema.md) | JSONSchema 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/
Read more
Ships withllamafarm

Enterprise AI capabilities on your own hardware. No cloud required. LlamaFarm is an open-source AI platform that runs entirely on your hardware.

Get the whole plugin
Stats
835
Stars
58
Forks
Maintained
Maintenance
Python
Language
Apache-2.0
License
2mo ago
Last commit
1y ago
Created

Repo: llama-farm/llamafarm