Agent skill

Fastapi Endpoint

by davila7 in davila7/claude-code-templates

Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests.

MITAuto-check passedBackend & APIs

Install Fastapi Endpoint

skills CLI
$ npx skills add davila7/claude-code-templates --skill fastapi-endpoint -a claude-code

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

GitHub CLI
$ gh skill install davila7/claude-code-templates fastapi-endpoint --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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/web-development/fastapi-endpoint .claude/skills/fastapi-endpoint && 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
fastapi-endpoint
GitHub stars
32k
Token cost
~3.9k tokens
SKILL.md length
443 words
Files
1
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests.

  • Works in 4 steps: Explore (Plan Mode) → Interview (AskUserQuestion) → Plan (ExitPlanMode) → …
  • Tasks that involve Backend development
  • SKILL.md covers When to use, Phase 1: Explore (Plan Mode), Phase 2: Interview… and Phase 3: Plan (ExitPlanMode), plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Fastapi Endpoint is an agent skill from davila7/claude-code-templates. Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs, covering Backend development, ORMs and data access and Design patterns. It works with FastAPI, SQLAlchemy, Pydantic and pytest. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Backend development
  • Tasks that involve ORMs and data access
  • Tasks that involve Design patterns

Example prompts

  • “/fastapi-endpoint”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Explore (Plan Mode)
  2. Interview (AskUserQuestion)
  3. Plan (ExitPlanMode)
  4. Execute

What it can do on your machine

Read from SKILL.md and the folder at commit 4c82aba. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    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

Fastapi Endpoint loads about 3.9k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 443 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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 passed

The automated check found no risky patterns in SKILL.md.

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 davila7/claude-code-templates at commit 4c82aba, republished under its MIT licence (© davila7). 443 words, ~3,863 tokens.

Download SKILL.mdSave it as .claude/skills/fastapi-endpoint/SKILL.md (or your agent's skills folder).
name
fastapi-endpoint
description
Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.
tags
fastapi, python, api, async, pydantic, sqlalchemy, backend

FastAPI Endpoint Builder

When to use

Use this skill when you need to:

  • Add new API endpoints to an existing FastAPI project
  • Build CRUD operations with proper validation and error handling
  • Set up authenticated endpoints with dependency injection
  • Create async database queries with SQLAlchemy 2.0
  • Generate complete test coverage for API routes

Phase 1: Explore (Plan Mode)

Enter plan mode. Before writing any code, explore the existing project to understand:

Project structure
  • Find the FastAPI app entry point (main.py, app.py, or app/__init__.py)
  • Identify the router organization pattern (single file vs routers/ directory)
  • Check for existing models/, schemas/, crud/, or services/ directories
  • Look at pyproject.toml or requirements.txt for installed dependencies
Existing patterns
  • How are existing endpoints structured? (function-based vs class-based)
  • What ORM is used? (SQLAlchemy 2.0 async, Tortoise, raw SQL, none)
  • How is the database session managed? (Depends(get_db), middleware, other)
  • What auth pattern exists? (OAuth2PasswordBearer, API key header, custom)
  • Are there existing Pydantic base models or shared schemas?
  • What response format is standard? (direct model, wrapped {"data": ..., "meta": ...})
Test patterns
  • Where do tests live? (tests/, test_*.py, *_test.py)
  • What test client is used? (httpx AsyncClient, TestClient, pytest-asyncio)
  • Are there test fixtures for database and auth?

Phase 2: Interview (AskUserQuestion)

Use AskUserQuestion to clarify requirements. Ask in rounds — do NOT dump all questions at once.

Round 1: Core endpoint
Question: "What resource does this endpoint manage?"
Header: "Resource"
Options:
  - "New resource (I'll describe the fields)" — Creating a new data model from scratch
  - "Existing model (extend it)" — Adding endpoints for a model that already exists in the codebase
  - "Relationship endpoint (nested)" — e.g., /users/{id}/orders — endpoint on a related resource

Question: "Which HTTP methods do you need?"
Header: "Methods"
multiSelect: true
Options:
  - "Full CRUD (GET list, GET detail, POST, PUT/PATCH, DELETE)" — All standard operations
  - "Read-only (GET list + GET detail)" — No mutations
  - "Custom action (POST /resource/{id}/action)" — Business logic endpoint, not standard CRUD
Round 2: Data model (if new resource)
Question: "What fields does the resource have? (describe briefly)"
Header: "Fields"
Options:
  - "Simple (< 6 fields, basic types)" — Strings, ints, booleans, dates
  - "Medium (6-15 fields, some relations)" — Includes foreign keys or enums
  - "Complex (nested objects, polymorphic)" — JSON fields, discriminated unions, computed fields
Round 3: Auth and access control
Question: "How should this endpoint be authenticated?"
Header: "Auth"
Options:
  - "JWT Bearer token (Recommended)" — OAuth2PasswordBearer with JWT decode
  - "API Key header" — X-API-Key header validation
  - "No auth (public)" — Open endpoint, no authentication required
  - "Use existing auth" — Reuse the auth dependency already in the project

Question: "Do you need role-based access control?"
Header: "RBAC"
Options:
  - "No — any authenticated user" — Single permission level
  - "Yes — role check (admin, user, etc.)" — Require specific roles per endpoint
  - "Yes — ownership check" — Users can only access their own resources
Round 4: Pagination, filtering, caching
Question: "What pagination style for list endpoints?"
Header: "Pagination"
Options:
  - "Cursor-based (Recommended)" — Best for real-time data, no offset drift
  - "Offset/limit" — Simple, good for admin panels with page numbers
  - "No pagination" — Small datasets, return all results

Question: "Do you need response caching?"
Header: "Caching"
Options:
  - "No caching" — Fresh data on every request
  - "Cache-Control headers" — Client-side caching via HTTP headers
  - "Redis/in-memory cache" — Server-side caching with TTL
Show full SKILL.md (209 more words)Show less

Phase 3: Plan (ExitPlanMode)

Write a concrete implementation plan covering:

  1. Files to create/modify — exact paths based on project structure discovered in Phase 1
  2. Pydantic schemas — Create, Update, Response, and List schemas with field types
  3. SQLAlchemy model — table name, columns, relationships, indexes
  4. CRUD/service layer — async functions for each operation
  5. Router — endpoint signatures, status codes, response models
  6. Dependencies — auth, pagination, filtering dependencies
  7. Tests — test cases for happy path, validation errors, auth failures, not found

Present via ExitPlanMode for user approval.

Phase 4: Execute

After approval, implement following this order:

Step 1: Pydantic schemas
python
from pydantic import BaseModel, ConfigDict
from datetime import datetime
from uuid import UUID

class ResourceBase(BaseModel):
    """Shared fields between create and response."""
    name: str
    # ... fields from interview

class ResourceCreate(ResourceBase):
    """Fields required to create the resource."""
    pass

class ResourceUpdate(BaseModel):
    """All fields optional for partial updates."""
    name: str | None = None

class ResourceResponse(ResourceBase):
    """Full resource with DB-generated fields."""
    model_config = ConfigDict(from_attributes=True)
    id: UUID
    created_at: datetime
    updated_at: datetime

class ResourceListResponse(BaseModel):
    """Paginated list response."""
    data: list[ResourceResponse]
    next_cursor: str | None = None
    has_more: bool
Step 2: SQLAlchemy model
python
from sqlalchemy import Column, String, DateTime, func
from sqlalchemy.dialects.postgresql import UUID as PG_UUID
import uuid
from app.database import Base

class Resource(Base):
    __tablename__ = "resources"

    id = Column(PG_UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    name = Column(String, nullable=False, index=True)
    created_at = Column(DateTime(timezone=True), server_default=func.now())
    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
Step 3: CRUD/service layer
python
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from uuid import UUID

async def get_resource(db: AsyncSession, resource_id: UUID) -> Resource | None:
    result = await db.execute(select(Resource).where(Resource.id == resource_id))
    return result.scalar_one_or_none()

async def list_resources(
    db: AsyncSession,
    cursor: str | None = None,
    limit: int = 20,
) -> tuple[list[Resource], str | None]:
    query = select(Resource).order_by(Resource.created_at.desc()).limit(limit + 1)
    if cursor:
        query = query.where(Resource.created_at < decode_cursor(cursor))
    result = await db.execute(query)
    items = list(result.scalars().all())
    next_cursor = encode_cursor(items[-1].created_at) if len(items) > limit else None
    return items[:limit], next_cursor

async def create_resource(db: AsyncSession, data: ResourceCreate) -> Resource:
    resource = Resource(**data.model_dump())
    db.add(resource)
    await db.commit()
    await db.refresh(resource)
    return resource

async def update_resource(
    db: AsyncSession, resource_id: UUID, data: ResourceUpdate
) -> Resource | None:
    resource = await get_resource(db, resource_id)
    if not resource:
        return None
    for field, value in data.model_dump(exclude_unset=True).items():
        setattr(resource, field, value)
    await db.commit()
    await db.refresh(resource)
    return resource

async def delete_resource(db: AsyncSession, resource_id: UUID) -> bool:
    resource = await get_resource(db, resource_id)
    if not resource:
        return False
    await db.delete(resource)
    await db.commit()
    return True
Step 4: Router with dependencies
python
from fastapi import APIRouter, Depends, HTTPException, Query, status
from sqlalchemy.ext.asyncio import AsyncSession
from uuid import UUID

router = APIRouter(prefix="/resources", tags=["resources"])

@router.get("", response_model=ResourceListResponse)
async def list_resources_endpoint(
    cursor: str | None = Query(None),
    limit: int = Query(20, ge=1, le=100),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),  # if auth required
):
    items, next_cursor = await list_resources(db, cursor=cursor, limit=limit)
    return ResourceListResponse(
        data=items,
        next_cursor=next_cursor,
        has_more=next_cursor is not None,
    )

@router.get("/{resource_id}", response_model=ResourceResponse)
async def get_resource_endpoint(
    resource_id: UUID,
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    resource = await get_resource(db, resource_id)
    if not resource:
        raise HTTPException(status_code=404, detail="Resource not found")
    return resource

@router.post("", response_model=ResourceResponse, status_code=status.HTTP_201_CREATED)
async def create_resource_endpoint(
    data: ResourceCreate,
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    return await create_resource(db, data)

@router.patch("/{resource_id}", response_model=ResourceResponse)
async def update_resource_endpoint(
    resource_id: UUID,
    data: ResourceUpdate,
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    resource = await update_resource(db, resource_id, data)
    if not resource:
        raise HTTPException(status_code=404, detail="Resource not found")
    return resource

@router.delete("/{resource_id}", status_code=status.HTTP_204_NO_CONTENT)
async def delete_resource_endpoint(
    resource_id: UUID,
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    deleted = await delete_resource(db, resource_id)
    if not deleted:
        raise HTTPException(status_code=404, detail="Resource not found")
Step 5: Tests
python
import pytest
from httpx import AsyncClient, ASGITransport
from app.main import app

@pytest.fixture
async def client():
    async with AsyncClient(
        transport=ASGITransport(app=app), base_url="http://test"
    ) as ac:
        yield ac

@pytest.mark.asyncio
async def test_create_resource(client: AsyncClient, auth_headers: dict):
    response = await client.post(
        "/resources",
        json={"name": "Test Resource"},
        headers=auth_headers,
    )
    assert response.status_code == 201
    data = response.json()
    assert data["name"] == "Test Resource"
    assert "id" in data

@pytest.mark.asyncio
async def test_get_resource_not_found(client: AsyncClient, auth_headers: dict):
    response = await client.get(
        "/resources/00000000-0000-0000-0000-000000000000",
        headers=auth_headers,
    )
    assert response.status_code == 404

@pytest.mark.asyncio
async def test_list_resources_pagination(client: AsyncClient, auth_headers: dict):
    # Create multiple resources first
    for i in range(5):
        await client.post(
            "/resources",
            json={"name": f"Resource {i}"},
            headers=auth_headers,
        )
    response = await client.get("/resources?limit=2", headers=auth_headers)
    assert response.status_code == 200
    data = response.json()
    assert len(data["data"]) == 2
    assert data["has_more"] is True
    assert data["next_cursor"] is not None

@pytest.mark.asyncio
async def test_create_resource_unauthorized(client: AsyncClient):
    response = await client.post("/resources", json={"name": "Test"})
    assert response.status_code in (401, 403)

@pytest.mark.asyncio
async def test_update_resource_partial(client: AsyncClient, auth_headers: dict):
    # Create
    create_resp = await client.post(
        "/resources",
        json={"name": "Original"},
        headers=auth_headers,
    )
    resource_id = create_resp.json()["id"]
    # Partial update
    response = await client.patch(
        f"/resources/{resource_id}",
        json={"name": "Updated"},
        headers=auth_headers,
    )
    assert response.status_code == 200
    assert response.json()["name"] == "Updated"

@pytest.mark.asyncio
async def test_delete_resource(client: AsyncClient, auth_headers: dict):
    create_resp = await client.post(
        "/resources",
        json={"name": "To Delete"},
        headers=auth_headers,
    )
    resource_id = create_resp.json()["id"]
    response = await client.delete(
        f"/resources/{resource_id}", headers=auth_headers
    )
    assert response.status_code == 204
    # Verify deleted
    get_resp = await client.get(
        f"/resources/{resource_id}", headers=auth_headers
    )
    assert get_resp.status_code == 404

Key patterns to follow

Dependency injection for auth
python
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="auth/token")

async def get_current_user(
    token: str = Depends(oauth2_scheme),
    db: AsyncSession = Depends(get_db),
) -> User:
    payload = decode_jwt(token)
    user = await db.get(User, payload["sub"])
    if not user:
        raise HTTPException(status_code=401, detail="Invalid token")
    return user

def require_role(*roles: str):
    """Factory for role-based access control."""
    async def checker(current_user: User = Depends(get_current_user)):
        if current_user.role not in roles:
            raise HTTPException(status_code=403, detail="Insufficient permissions")
        return current_user
    return checker
Cursor-based pagination helper
python
import base64
from datetime import datetime

def encode_cursor(dt: datetime) -> str:
    return base64.urlsafe_b64encode(dt.isoformat().encode()).decode()

def decode_cursor(cursor: str) -> datetime:
    return datetime.fromisoformat(base64.urlsafe_b64decode(cursor).decode())
Error responses

Always use FastAPI's HTTPException with consistent detail messages. For validation errors, Pydantic v2 handles them automatically via RequestValidationError (422).

python
# 404 — not found
raise HTTPException(status_code=404, detail="Resource not found")

# 409 — conflict (duplicate)
raise HTTPException(status_code=409, detail="Resource with this name already exists")

# 403 — forbidden
raise HTTPException(status_code=403, detail="Not allowed to modify this resource")

Checklist before finishing

  • All endpoints return proper status codes (201 for POST, 204 for DELETE)
  • Pydantic schemas use model_config = ConfigDict(from_attributes=True) for ORM mode
  • List endpoint has pagination with configurable limit
  • Auth dependency is applied to all non-public endpoints
  • Tests cover: happy path, not found, unauthorized, validation errors
  • Router is registered in the main FastAPI app
  • Database model has proper indexes on filtered/sorted columns

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-tool/components/skills/web-development/fastapi-endpoint of davila7/claude-code-templates.

Open the folder on GitHubat commit 4c82aba

Compare with similar skills

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Fastapi Endpoint compared with similar skills
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Categories

Questions about Fastapi Endpoint

What does Fastapi Endpoint do?

Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Fastapi Endpoint is an agent skill from davila7/claude-code-templates. Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests.

When should I use Fastapi Endpoint?

Fastapi Endpoint fits situations like: tasks that involve Backend development; tasks that involve ORMs and data access; tasks that involve Design patterns.

How do I install Fastapi Endpoint in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill fastapi-endpoint -a claude-code`. Or copy the skill folder (cli-tool/components/skills/web-development/fastapi-endpoint in davila7/claude-code-templates) into .claude/skills/fastapi-endpoint in your project. Claude Code loads it when a task matches its description.

How do I install Fastapi Endpoint in Codex?

Run `npx skills add davila7/claude-code-templates --skill fastapi-endpoint -a codex`. Or copy the skill folder (cli-tool/components/skills/web-development/fastapi-endpoint in davila7/claude-code-templates) into .agents/skills/fastapi-endpoint in your project. Codex loads it when a task matches its description.

Can I use Fastapi Endpoint 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 davila7/claude-code-templates --skill fastapi-endpoint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fastapi-endpoint, .gemini/skills/fastapi-endpoint, .github/skills/fastapi-endpoint and .opencode/skills/fastapi-endpoint in your project.

What does Fastapi Endpoint need to run?

SKILL.md names no scripts, command-line tools or credentials: Fastapi Endpoint is instructions for the agent only. Our summary lists: Python 3.

Does Fastapi Endpoint 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 Fastapi Endpoint safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fastapi Endpoint use?

Fastapi Endpoint is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fastapi Endpoint use?

About 3.9k tokens (SKILL.md is roughly 15k 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 Fastapi Endpoint?

Skills that share tags, products or a category with Fastapi Endpoint: Mastering Python Skill (SpillwaveSolutions/agent-brain, 120 stars), Python (MadAppGang/claude-code, 283 stars), FastAPI Expert (Jeffallan/claude-skills, 12k stars) and Fastapi Patterns (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fastapi Endpoint?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,432 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 7, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.