fastapi-clean-architecture
Build modular FastAPI applications using Clean Architecture, including domain-driven design, dependency injection, repository patterns, and testing strategies for scalable Python backend services.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
412 skills found
Build modular FastAPI applications using Clean Architecture, including domain-driven design, dependency injection, repository patterns, and testing strategies for scalable Python backend services.
Reverse engineer web APIs by capturing browser traffic (HAR files) and generating production-ready Python API clients for automation and data extraction.
Expert guidance for Claude Messages API: structured outputs, prompt caching, tool use, and migration from deprecated Claude 3.x models to 4.5. Prevents common API errors.
Base ecosystem skill for Refly. Creates, discovers, and runs domain-specific skills, routes user intent to workflows via symlinks, and automates multi-step pipelines via the Refly CLI.
Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.
A RAG-based AI solver for high school Chinese GSAT exams, featuring structured knowledge retrieval, reasoning templates, and explainable AI outputs.
A design system and anti-pattern guide to make AI-generated UI look human-crafted. Ensures professional aesthetics by managing color, typography, spacing, and animations for the Toh Framework.
Create high-performance AI skills by reverse-engineering successful GitHub projects and proven open-source methodologies.
Analyze and audit React projects for security, performance, correctness, and architecture issues with actionable diagnostics and scoring.
Generate AGENTS.md and AI configuration files (Cursor, Claude, Gemini, Copilot) for your project to streamline your vibe-coding workflow and maintain context across sessions.
Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.
Explains code using visual diagrams, relatable analogies, step-by-step walkthroughs, and common pitfalls.