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python-fastapi-development
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | python-fastapi-development |
| description | Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns. |
| category | granular-workflow-bundle |
| risk | safe |
| source | personal |
| date_added | 2026-02-27 |
Specialized workflow for building production-ready Python backends with FastAPI, featuring async patterns, SQLAlchemy ORM, Pydantic validation, and comprehensive API patterns.
Use this workflow when:
app-builder - Application scaffoldingpython-development-python-scaffold - Python scaffoldingfastapi-templates - FastAPI templatesuv-package-manager - Package managementUse @fastapi-templates to scaffold a new FastAPI project
Use @python-development-python-scaffold to set up Python project structure
prisma-expert - Prisma ORM (alternative)database-design - Schema designpostgresql - PostgreSQL setuppydantic-models-py - Pydantic modelsUse @database-design to design PostgreSQL schema
Use @pydantic-models-py to create Pydantic models for API
fastapi-router-py - FastAPI routersapi-design-principles - API designapi-patterns - API patternsUse @fastapi-router-py to create API endpoints with CRUD operations
Use @api-design-principles to design RESTful API
auth-implementation-patterns - Authenticationapi-security-best-practices - API securityUse @auth-implementation-patterns to implement JWT authentication
fastapi-pro - FastAPI patternserror-handling-patterns - Error handlingUse @fastapi-pro to implement comprehensive error handling
python-testing-patterns - pytest testingapi-testing-observability-api-mock - API testingUse @python-testing-patterns to write pytest tests for FastAPI
api-documenter - API documentationopenapi-spec-generation - OpenAPI specsUse @api-documenter to generate comprehensive API documentation
deployment-engineer - Deploymentdocker-expert - ContainerizationUse @docker-expert to containerize FastAPI application
| Category | Technology |
|---|---|
| Framework | FastAPI |
| Language | Python 3.11+ |
| ORM | SQLAlchemy 2.0 |
| Validation | Pydantic v2 |
| Database | PostgreSQL |
| Migrations | Alembic |
| Auth | JWT, OAuth2 |
| Testing | pytest |
development - General developmentdatabase - Database operationssecurity-audit - Security testingapi-development - API patternsMulti-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.
12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
Helps an AI assistant work with the Miraheze wiki farm — writing wiki requests that get approved, navigating ManageWiki, doing common how-to tasks (templates, skins, permissions, custom domains, backups), writing regex for MediaWiki search-and-replace, AND writing actual article and page content for Miraheze-hosted wikis. Miraheze does NOT ban generative AI; AI-written content is permitted (subject to per-wiki rules). MADE BY SQERSTERS
Helps an AI assistant write, structure, and edit articles in the encyclopedic style of Wikipedia — neutral tone, lead section, summary style, inline citations, no peacock/weasel/persuasive language, and the stub→FA quality ladder. Also covers Simple English Wikipedia rules. MADE BY SQERSTERS