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Agent skill

Server Skills

by llama-farm in llama-farm/llamafarm

Server-specific best practices for FastAPI, Celery, and Pydantic.

Apache-2.0Auto-check: notesBackend & APIs

Install Server Skills

skills CLI
$ npx skills add llama-farm/llamafarm --skill server-skills -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install llama-farm/llamafarm server-skills --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/llama-farm/llamafarm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/server-skills .claude/skills/server-skills && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
server-skills
GitHub stars
836
Token cost
~1.3k tokens
SKILL.md length
233 words
Files
5
Skills in repo
19
Repo updated
First seen
Licence
Apache-2.0

At a glance

Server-specific best practices for FastAPI, Celery, and Pydantic.

  • Works in 4 steps: FastAPI Routes (High priority) → Celery Tasks (High priority) → Pydantic Models (Medium priority) → …
  • Tasks that involve Background jobs
  • SKILL.md covers Overview, Links to Shared Skills, Server-Specific Checklists and Architecture Overview, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Server Skills is an agent skill from llama-farm/llamafarm. Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `celery.md`, `fastapi.md` and `performance.md`).

It sits in Backend & APIs, covering Background jobs and Backend development. It works with Pydantic, Python and FastAPI. The repository describes itself as: Deploy any AI model, agent, database, RAG, and pipeline locally or remotely in minutes. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Background jobs
  • Tasks that involve Backend development

Example prompts

  • “/server-skills”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. FastAPI Routes (High priority)
  2. Celery Tasks (High priority)
  3. Pydantic Models (Medium priority)
  4. Performance (Medium priority)

What it can do on your machine

Read from SKILL.md and the folder at commit 6244d46. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Server Skills loads about 1.3k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 233 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:78
    class Settings(BaseSettings, env_file=".env"):
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from llama-farm/llamafarm at commit 6244d46, republished under its Apache-2.0 licence (© llama-farm). 233 words, ~1,328 tokens.

Download SKILL.mdSave it as .claude/skills/server-skills/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
server-skills
description
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
allowed-tools
Read, Grep, Glob, Bash
user-invocable
false

Server Skills for LlamaFarm

Framework-specific patterns and code review checklists for the LlamaFarm Server component.

Overview

PropertyValue
Pathserver/
Python3.12+
FrameworkFastAPI 0.116+
Task QueueCelery 5.5+
ValidationPydantic 2.x, pydantic-settings
Loggingstructlog with FastAPIStructLogger

This skill extends the shared Python skills. See:

Server-Specific Checklists

TopicFileKey Points
FastAPIfastapi.mdRoutes, dependencies, middleware, exception handlers
Celerycelery.mdTask patterns, error handling, retries, signatures
Pydanticpydantic.mdPydantic v2 models, validation, serialization
Performanceperformance.mdAsync 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)
python
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
python
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
python
# 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
python
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

  1. 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

  2. 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

  3. Pydantic Models (Medium priority)

    • Use Pydantic v2 patterns (model_config, Field)
    • Proper validation with field constraints
    • Serialization with model_dump()

  4. 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.

© llama-farm, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in .claude/skills/server-skills of llama-farm/llamafarm.

  • SKILL.md
  • celery.md
  • fastapi.md
  • performance.md
  • pydantic.md

Open the folder on GitHubat commit 6244d46

Compare with similar skills

Server Skills next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Server Skills compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Server Skills this skillllama-farm/llamafarm836—~1.3kAutomated safety check: NotesApache-2.0
Fastcrudbenavlabs/fastcrud1.6k—~5kAutomated safety check: PassMIT
FbaZhongye1/KnowAgenticRAG135—~758Automated safety check: PassNone
Fastapi Appccplugins/awesome-claude-code-plugins970—~1.1kAutomated safety check: NotesApache-2.0
Framework Migration AssistantArabelaTso/Skills-4-SE253—~1.9kAutomated safety check: PassApache-2.0
Sentry Python SDKgetsentry/sentry-for-ai268—~4.1kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Server Skills

What does Server Skills do?

Server-specific best practices for FastAPI, Celery, and Pydantic. Server Skills is an agent skill from llama-farm/llamafarm. Server-specific best practices for FastAPI, Celery, and Pydantic.

When should I use Server Skills?

Server Skills fits situations like: tasks that involve Background jobs; tasks that involve Backend development.

How do I install Server Skills in Claude Code?

Run `npx skills add llama-farm/llamafarm --skill server-skills -a claude-code`. Or copy the skill folder (.claude/skills/server-skills in llama-farm/llamafarm) into .claude/skills/server-skills in your project. Claude Code loads it when a task matches its description.

How do I install Server Skills in Codex?

Run `npx skills add llama-farm/llamafarm --skill server-skills -a codex`. Or copy the skill folder (.claude/skills/server-skills in llama-farm/llamafarm) into .agents/skills/server-skills in your project. Codex loads it when a task matches its description.

Can I use Server Skills in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add llama-farm/llamafarm --skill server-skills -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/server-skills, .gemini/skills/server-skills, .github/skills/server-skills and .opencode/skills/server-skills in your project.

What does Server Skills need to run?

SKILL.md names no scripts, command-line tools or credentials: Server Skills is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash.

Does Server Skills access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Server Skills safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Server Skills use?

Server Skills is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Server Skills use?

About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Server Skills?

Skills that share tags, products or a category with Server Skills: Fastcrud (benavlabs/fastcrud, 1.6k stars), Fba (Zhongye1/KnowAgenticRAG, 135 stars), Fastapi App (ccplugins/awesome-claude-code-plugins, 970 stars) and Framework Migration Assistant (ArabelaTso/Skills-4-SE, 253 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Server Skills?

llama-farm (a GitHub organization) maintains it in llama-farm/llamafarm, which has 836 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on June 10, 2026.

Source: llama-farm/llamafarm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.