Python/FastAPI Development Workflow

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/python-fastapi-development

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Python/FastAPI Development Workflow

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Technology Stack, Quality Gates, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Building new REST APIs with FastAPI
• Creating async Python backends
• Implementing database integration with SQLAlchemy
• Setting up API authentication
• Developing microservices
• Use when the request clearly matches the imported source intent: Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. app-builder - Application scaffolding
2. python-development-python-scaffold - Python scaffolding
3. fastapi-templates - FastAPI templates
4. uv-package-manager - Package management
5. Set up Python environment (uv/poetry)
6. Create project structure
7. Configure FastAPI app

Imported Workflow Notes

Imported: Workflow Phases

Phase 1: Project Setup

Skills to Invoke

• app-builder

  - Application scaffolding

• python-development-python-scaffold

  - Python scaffolding

• fastapi-templates

  - FastAPI templates

• uv-package-manager

  - Package management

Actions

1. Set up Python environment (uv/poetry)
2. Create project structure
3. Configure FastAPI app
4. Set up logging
5. Configure environment variables

Copy-Paste Prompts

Use @fastapi-templates to scaffold a new FastAPI project

Use @python-development-python-scaffold to set up Python project structure

Phase 2: Database Setup

Skills to Invoke

• prisma-expert

  - Prisma ORM (alternative)

• database-design

  - Schema design

• postgresql

  - PostgreSQL setup

• pydantic-models-py

  - Pydantic models

Actions

1. Design database schema
2. Set up SQLAlchemy models
3. Create database connection
4. Configure migrations (Alembic)
5. Set up session management

Copy-Paste Prompts

Use @database-design to design PostgreSQL schema

Use @pydantic-models-py to create Pydantic models for API

Phase 3: API Routes

Skills to Invoke

• fastapi-router-py

  - FastAPI routers

• api-design-principles

  - API design

• api-patterns

  - API patterns

Actions

1. Design API endpoints
2. Create API routers
3. Implement CRUD operations
4. Add request validation
5. Configure response models

Copy-Paste Prompts

Use @fastapi-router-py to create API endpoints with CRUD operations

Use @api-design-principles to design RESTful API

Phase 4: Authentication

Skills to Invoke

• auth-implementation-patterns

  - Authentication

• api-security-best-practices

  - API security

Actions

1. Choose auth strategy (JWT, OAuth2)
2. Implement user registration
3. Set up login endpoints
4. Create auth middleware
5. Add password hashing

Copy-Paste Prompts

Use @auth-implementation-patterns to implement JWT authentication

Phase 5: Error Handling

Skills to Invoke

• fastapi-pro

  - FastAPI patterns

• error-handling-patterns

  - Error handling

Actions

1. Create custom exceptions
2. Set up exception handlers
3. Implement error responses
4. Add request logging
5. Configure error tracking

Copy-Paste Prompts

Use @fastapi-pro to implement comprehensive error handling

Phase 6: Testing

Skills to Invoke

• python-testing-patterns

  - pytest testing

• api-testing-observability-api-mock

  - API testing

Actions

1. Set up pytest
2. Create test fixtures
3. Write unit tests
4. Implement integration tests
5. Configure test database

Copy-Paste Prompts

Use @python-testing-patterns to write pytest tests for FastAPI

Phase 7: Documentation

Skills to Invoke

• api-documenter

  - API documentation

• openapi-spec-generation

  - OpenAPI specs

Actions

1. Configure OpenAPI schema
2. Add endpoint documentation
3. Create usage examples
4. Set up API versioning
5. Generate API docs

Copy-Paste Prompts

Use @api-documenter to generate comprehensive API documentation

Phase 8: Deployment

Skills to Invoke

• deployment-engineer

  - Deployment

• docker-expert

  - Containerization

Actions

1. Create Dockerfile
2. Set up docker-compose
3. Configure production settings
4. Set up reverse proxy
5. Deploy to cloud

Copy-Paste Prompts

Use @docker-expert to containerize FastAPI application

Imported: Related Workflow Bundles

• development

  - General development

• database

  - Database operations

• security-audit

  - Security testing

• api-development

  - API patterns

Imported: Overview

Specialized workflow for building production-ready Python backends with FastAPI, featuring async patterns, SQLAlchemy ORM, Pydantic validation, and comprehensive API patterns.

Imported: Technology Stack

Examples

Example 1: Ask for the upstream workflow directly

Use @python-fastapi-development to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @python-fastapi-development against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @python-fastapi-development for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @python-fastapi-development using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/python-fastapi-development

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @prompt-engineer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-library

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Quality Gates

• All tests passing (>80% coverage)
• Type checking passes (mypy)
• Linting clean (ruff, black)
• API documentation complete
• Security scan passed
• Performance benchmarks met

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.