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agenticgogol
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backend-fastapi

Implements FastAPI backend endpoints exactly matching the API contract defined in design.md.

1|9|Updated Jul 11, 2026
Search Tags:#fastapi#python#backend#scaffolding#rest api#uvicorn#api contract
One-click install
npx skills add https://github.com/agenticgogol/Edureka_Coding_Agent_Enabled_Demo_11Jul --skill backend-fastapi-agenticgogol
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backend-fastapi
Source: https://github.com/agenticgogol/Edureka_Coding_Agent_Enabled_Demo_11Jul/tree/main/.claude/skills/backend-fastapi
Command: npx skills add https://github.com/agenticgogol/Edureka_Coding_Agent_Enabled_Demo_11Jul --skill backend-fastapi-agenticgogol

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, uvicorn.

What problem does it solve? When a project plan calls for an HTTP API, developers need a backend that matches the agreed design exactly rather than drifting into ad-hoc endpoints. This Skill scaffolds and implements a FastAPI backend that follows the API contract in design.md precisely, keeping frontend and backend in sync. ## Core Features & Use Cases - Contract-Exact Implementation: Implements every endpoint from design.md's API contract with identical paths, methods, and request/response shapes, and flags missing endpoints instead of inventing them. - Agent Integration: Wires agent or graph logic from agent-* skills (LangGraph, CrewAI, DSPy, MCP, GraphRAG) via clean imports rather than embedding graph logic inline. - Real-Key Configuration: Loads secrets through a shared config loader with no mock-mode fallback, relying on prior API key verification. - Use Case: After design.md defines a /chat POST endpoint for a LangGraph-powered app, use this Skill to scaffold projects/<slug>/backend/ with main.py, routers, error handling, and a uvicorn run snippet. ## Quick Start Ask the agent to build the FastAPI backend for the current project according to the API contract in design.md.

Frequently Asked Questions about backend-fastapi

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a FastAPI backend from a design document?▼

Scaffold a backend directory with main.py and routers, then implement every endpoint listed in the design document's API contract with matching paths, methods, and request/response shapes. Add a uvicorn main:app --reload run snippet for local startup.

How do I connect a FastAPI backend to a LangGraph or CrewAI agent?▼

Import the agent or graph logic from the module produced by the relevant agent skill and call it from your route handlers. The backend should invoke the agent through a clean import rather than embedding graph-building code inline.

What should I do if the frontend needs an endpoint not in the design?▼

Stop and flag the missing endpoint back to the design step instead of inventing it. Adding undocumented endpoints forces the frontend to guess at request and response shapes, which breaks the contract between layers.

Does this FastAPI setup work without a real API key?▼

No. There is no mock-mode fallback; every external call to an LLM or vector database requires a real, verified key. A separate key-verification step runs before the backend is built, and verification runs against the real provider.

How much error handling should a FastAPI backend include?▼

Handle validation errors at the API boundary by returning 4xx responses with clear messages. Avoid adding defensive handling for scenarios the design document does not specify, since that adds untested complexity.

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