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.
Best use case
fastapi-templates is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using fastapi-templates should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fastapi-templates/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How fastapi-templates Compares
| Feature / Agent | fastapi-templates | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# FastAPI Project Templates Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs. ## Use this skill when - Starting new FastAPI projects from scratch - Implementing async REST APIs with Python - Building high-performance web services and microservices - Creating async applications with PostgreSQL, MongoDB - Setting up API projects with proper structure and testing ## Do not use this skill when - The task is unrelated to fastapi project templates - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns and examples.
Related Skills
python-fastapi-development
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
fastapi-router-py
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...
fastapi-pro
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns.
shabbat-times
Access Jewish calendar data and Shabbat times via Hebcal API. Use when building apps with Shabbat times, Jewish holidays, Hebrew dates, or Zmanim. Triggers on Shabbat times, Hebcal, Jewish calendar, Hebrew date, Zmanim.
mcp:setup-serena-mcp
Guide for setup Serena MCP server for semantic code retrieval and editing capabilities
mcp:setup-context7-mcp
Guide for setup Context7 MCP server to load documentation for specific technologies.
server-management
Server management principles and decision-making. Process management, monitoring strategy, and scaling decisions. Teaches thinking, not commands.
serpapi-automation
Automate Serpapi tasks via Rube MCP (Composio). Always search tools first for current schemas.
segment-cdp
Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance ...
seatbelt-sandboxer
Generates minimal macOS Seatbelt sandbox configurations. Use when sandboxing, isolating, or restricting macOS applications with allowlist-based profiles.
scvi-tools
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
scrapingbee-automation
Automate Scrapingbee tasks via Rube MCP (Composio). Always search tools first for current schemas.