server-skills
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
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- Last 24h
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/llama-farm/llamafarm --skill server-skills- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads server-skills automatically when relevant.
- 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.
Server Skills for LlamaFarm
Framework-specific patterns and code review checklists for the LlamaFarm Server component.
Overview
| Property | Value |
|---|---|
| Path | server/ |
| Python | 3.12+ |
| Framework | FastAPI 0.116+ |
| Task Queue | Celery 5.5+ |
| Validation | Pydantic 2.x, pydantic-settings |
| Logging | structlog with FastAPIStructLogger |
Links to Shared Skills
This skill extends the shared Python skills. See:
- Python Patterns - Dataclasses, comprehensions, imports
- Async Patterns - async/await, asyncio, concurrency
- Typing Patterns - Type hints, generics, Pydantic
- Testing Patterns - Pytest, fixtures, mocking
- Error Handling - Exceptions, logging, context managers
- Security Patterns - Path traversal, injection, secrets
Server-Specific Checklists
| Topic | File | Key Points |
|---|---|---|
| FastAPI | fastapi.md | Routes, dependencies, middleware, exception handlers |
| Celery | celery.md | Task patterns, error handling, retries, signatures |
| Pydantic | pydantic.md | Pydantic v2 models, validation, serialization |
| Performance | performance.md | Async patterns, caching, connection pooling |
Architecture Overview
server/
├── main.py # Uvicorn entry point, MCP mount
├── api/
│ ├── main.py # FastAPI app factory, middleware setup
│ ├── errors.py # Custom exceptions + exception handlers
│ ├── middleware/ # ASGI middleware (structlog, errors)
│ └── routers/ # API route modules
│ ├── projects/ # Project CRUD endpoints
│ ├── datasets/ # Dataset management
│ ├── rag/ # RAG query endpoints
│ └── ...
├── core/
│ ├── settings.py # pydantic-settings configuration
│ ├── logging.py # structlog setup, FastAPIStructLogger
│ └── celery/ # Celery app configuration
│ ├── celery.py # Celery app instance
│ └── rag_client.py # RAG task signatures and helpers
├── services/ # Business logic layer
│ ├── project_service.py # Project CRUD operations
│ ├── dataset_service.py # Dataset management
│ └── ...
├── agents/ # AI agent implementations
└── tests/ # Pytest test suite
Quick Reference
Settings Pattern (pydantic-settings)
from pydantic_settings import BaseSettings
class Settings(BaseSettings, env_file=".env"):
HOST: str = "0.0.0.0"
PORT: int = 14345
LOG_LEVEL: str = "INFO"
settings = Settings() # Module-level singleton
Structured Logging
from core.logging import FastAPIStructLogger
logger = FastAPIStructLogger(__name__)
logger.info("Operation completed", extra={"count": 10, "duration_ms": 150})
logger.bind(namespace=namespace, project=project_id) # Add context
Custom Exceptions
# Define exception hierarchy
class NotFoundError(Exception): ...
class ProjectNotFoundError(NotFoundError):
def __init__(self, namespace: str, project_id: str):
self.namespace = namespace
self.project_id = project_id
super().__init__(f"Project {namespace}/{project_id} not found")
# Register handler in api/errors.py
async def _handle_project_not_found(request: Request, exc: Exception) -> Response:
payload = ErrorResponse(error="ProjectNotFound", message=str(exc))
return JSONResponse(status_code=404, content=payload.model_dump())
def register_exception_handlers(app: FastAPI) -> None:
app.add_exception_handler(ProjectNotFoundError, _handle_project_not_found)
Service Layer Pattern
class ProjectService:
@classmethod
def get_project(cls, namespace: str, project_id: str) -> Project:
project_dir = cls.get_project_dir(namespace, project_id)
if not os.path.isdir(project_dir):
raise ProjectNotFoundError(namespace, project_id)
# ... load and validate
Review Checklist Summary
-
FastAPI Routes (High priority)
- Proper async/sync function choice
- Response model defined with
response_model= - OpenAPI metadata (operation_id, tags, summary)
- HTTPException with proper status codes
-
Celery Tasks (High priority)
- Use signatures for cross-service calls
- Implement proper timeout and polling
- Handle task failures gracefully
- Store group metadata for parallel tasks
-
Pydantic Models (Medium priority)
- Use Pydantic v2 patterns (model_config, Field)
- Proper validation with field constraints
- Serialization with model_dump()
-
Performance (Medium priority)
- Avoid blocking calls in async functions
- Use proper connection pooling for external services
- Implement caching where appropriate
See individual topic files for detailed checklists with grep patterns.
FAQ
What does the server-skills skill do?
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
How do I install server-skills?
Run npx skills add https://github.com/llama-farm/llamafarm --skill server-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/server-skills/ (personal) or .claude/skills/server-skills/ (project).
Which AI agents can use server-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 server-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 server-skills?
It is published by llama-farm in llama-farm/llamafarm. It has 45 recorded installs.
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