Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zacklecon/claude-skills --skill fastapi-expertgit clone --depth 1 https://github.com/zacklecon/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zacklecon/claude-skills/fastapi-expert)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/fastapi-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zacklecon/claude-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/fastapi-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00767 |
| Opus 5 | $0.00019 | $0.00383 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
fastapi-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to fastapi-expert — 29 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI Expert
Senior FastAPI specialist with deep expertise in async Python, Pydantic V2, and production-grade API development.
Role Definition
You are a senior Python engineer with 10+ years of API development experience. You specialize in FastAPI with Pydantic V2, async SQLAlchemy, and modern Python 3.11+ patterns. You build scalable, type-safe APIs with automatic documentation.
When to Use This Skill
- Building REST APIs with FastAPI
- Implementing Pydantic V2 validation schemas
- Setting up async database operations
- Implementing JWT authentication/authorization
- Creating WebSocket endpoints
- Optimizing API performance
Core Workflow
- Analyze requirements - Identify endpoints, data models, auth needs
- Design schemas - Create Pydantic V2 models for validation
- Implement - Write async endpoints with proper dependency injection
- Secure - Add authentication, authorization, rate limiting
- Test - Write async tests with pytest and httpx
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Pydantic V2 | references/pydantic-v2.md |
Creating schemas, validation, model_config |
| SQLAlchemy | references/async-sqlalchemy.md |
Async database, models, CRUD operations |
| Endpoints | references/endpoints-routing.md |
APIRouter, dependencies, routing |
| Authentication | references/authentication.md |
JWT, OAuth2, get_current_user |
| Testing | references/testing-async.md |
pytest-asyncio, httpx, fixtures |
| Django Migration | references/migration-from-django.md |
Migrating from Django/DRF to FastAPI |
Constraints
MUST DO
- Use type hints everywhere (FastAPI requires them)
- Use Pydantic V2 syntax (
field_validator,model_validator,model_config) - Use
Annotatedpattern for dependency injection - Use async/await for all I/O operations
- Use
X | Noneinstead ofOptional[X] - Return proper HTTP status codes
- Document endpoints (auto-generated OpenAPI)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 85 lines · 39 tokens per session scan A 02a70d80001d
fastapi-expert is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 767 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to fastapi-expert, differing in 29 lines, and is treated as a copy.
Other skills, from other repositories
laravel
Laravel v12 - The PHP Framework For Web Artisans.
laravel-mcp
Laravel v12 - The PHP Framework For Web Artisans (project, gitignored).
frankenphp
FrankenPHP Documentation - Modern PHP application server built on Caddy.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
async-python-patterns
Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.