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.
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427 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.
GitHub operations via gh CLI. Use for repository inspection, issues, PRs, releases, and deep codebase analysis including cloning for architectural insights.
Perform cohort analysis on user engagement data. Identify retention trends, feature adoption rates, churn patterns, and generate actionable research recommendations through quantitative data analysis.
Foundry development guide for CMTAT RuleEngine contracts, including testing, deployment scripts, and project-specific Solidity patterns.
Design professional-grade brand identities using geometric primitives, negative space, and flat vector-style aesthetics via AI-driven branding logic.
A connectionless, HTTP-based rate limiting SDK for TypeScript, ideal for serverless, edge functions, and distributed environments using Upstash Redis.
Optimize Node.js performance via Redis caching, clustering, profiling, and monitoring to build fast, scalable, and efficient backend services.
A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.
Integrates browser-native Proofreader API into web applications for AI-powered text correction, grammar checking, and language support with managed model lifecycle.
A precision-focused UX/UI engineering agent for identifying bugs, optimizing usability, and ensuring flawless interface performance in workflow applications.
A structured prompting framework to transform casual inputs into professional, modular LLM prompts with persona, context, task, format, and guardrails.
Automates the documentation of solved technical issues using YAML frontmatter, categorized directories, and institutional knowledge indexing for JUCE plugin development.