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FastAPI best practices and conventions for clean, modern API development. \n \n Use Annotated for all parameter and dependency declarations to maintain type safety, reusability, and compatibility with other contexts \n Declare return types or response models on path operations to enable automatic validation, filtering, serialization, and OpenAPI documentation \n Use async def only when calling async code with await ; default to regular def for blocking operations to avoid event loop blocking \n

Jest testing best practices for JavaScript and TypeScript projects with mocking, async handling, and React patterns. \n \n Organize tests with descriptive names in nested describe blocks, using .test.ts / .test.js files placed alongside source code or in __tests__ directories \n Mock external dependencies with jest.mock() , jest.spyOn() , and mockImplementation() , resetting between tests to prevent state leakage \n Handle async code with promises, async/await , and resolves / rejects matchers;

Analyze and rewrite tweets to maximize reach using Twitter's open-source recommendation algorithms. \n \n Applies Real-graph, SimClusters, TwHIN, and Tweepcred models to identify optimization opportunities and explain algorithmic ranking decisions \n Identifies engagement signals (likes, replies, retweets, bookmarks) and negative signals (blocks, reports, mutes) that affect tweet distribution \n Rewrites tweets to trigger specific engagement patterns, target community resonance, and strengthen t

Systematic Go code review against community style standards and best practices. \n \n Covers 15+ review categories: formatting, documentation, error handling, naming, concurrency, interfaces, data structures, security, declarations, functions, style, logging, imports, generics, and testing \n Includes automated pre-review checks via gofmt , go vet , and golangci-lint to catch mechanical issues before manual review \n Organizes findings by severity (must-fix, should-fix, nit) using a consistent t

Master Java concurrency patterns for thread-safe applications.

Generate high-quality AI images using Volcengine's Jimeng 4.0 API with text prompts or image inputs.

Extract product data, prices, reviews, and seller information from 50+ e-commerce marketplaces. \n \n Three workflow modes: Products & Pricing (price tracking, competitor analysis), Customer Reviews (sentiment analysis, quality issues), and Seller Intelligence (vendor discovery via Google Shopping) \n Supports Amazon (20+ regions), Walmart, eBay, IKEA, Costco, and European retailers; input via product URLs, category URLs, or keyword search \n Optional AI-powered analysis generates insights

Modern Python project setup with uv, ruff, and ty for Python 3.11+. \n \n Replaces pip, Poetry, black, flake8, mypy, and pre-commit with faster, simpler alternatives from the Astral team \n Covers new project creation, dependency management via uv add / uv remove , and linting/formatting/type-checking workflows \n Includes migration paths from legacy tooling (requirements.txt, setup.py, flake8+black+isort, mypy/pyright) \n Provides decision tree for single-file scripts (PEP 723), simple projects

Web scraping and data extraction using Python tools for static, dynamic, and large-scale content. \n \n Supports static sites via requests and BeautifulSoup, dynamic content via Selenium and Playwright, and large-scale extraction via Scrapy and firecrawl \n Includes specialized tools for AI-powered extraction (jina), structured queries (agentQL), and complex automation workflows (multion) \n Built-in guidance on rate limiting, robots.txt compliance, error handling, session management, and pagina