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Found 5,337 Skills
Use when the user needs full UI/UX design intelligence — styles, palettes, fonts, UX guidelines, chart selection, and accessible, performant implementation across any supported stack. Triggers: user says "design", "UI", "UX", "color palette", "typography", "accessibility", "responsive design", "chart type", "style guide", building any user-facing interface.
A-share multi-agent AI investment research and analysis tool - 15 AI analysts collaborate to complete technical analysis, fundamental analysis, market sentiment judgment, capital flow tracking (northbound capital/main capital), macroeconomic analysis and game theory deduction, and output structured trading suggestions and risk assessment. Supports Shanghai and Shenzhen A-share stock codes and Chinese names. Multi-agent AI stock analysis for China A-shares. 15 specialized analysts collaborate across technical analysis, fundamental analysis, sentiment analysis, smart money flow tracking, macro economics, and game theory to deliver structured buy/sell/hold recommendations with risk assessment.
Automated semantic versioning and release workflow for Claude Code plugins. Handles version increments across package.json, marketplace.json, and plugin.json, build verification, git tagging, GitHub releases, and changelog generation.
Uses the TweetSmash REST API to fetch bookmarks, inspect labels, and add or remove labels from saved tweets. Use when integrating TweetSmash into scripts, agents, workflows, cron jobs, or internal tools that need bookmark retrieval, filtering, pagination, or label management. Do not use for direct browser automation inside TweetSmash, unrelated X or Twitter APIs, or tasks that only need product marketing copy.
AI-powered Uniswap developer tools: trading, hooks, drivers, and on-chain analysis across V2/V3/V4
The Twelve-Factor App methodology for building scalable, maintainable cloud-native applications. Use when designing backend services, APIs, microservices, or any software-as-a-service application. Triggers on deployment patterns, configuration management, process architecture, logging, and infrastructure decisions.
USDC is Circle's stablecoin deployed across multiple blockchain ecosystems including EVM chains (Ethereum, Base, Arbitrum, Polygon, Arc) and Solana. Use this skill to check balances, send transfers, approve spending, and verify transactions. Triggers on: USDC balance, send USDC, transfer USDC, approve USDC, USDC allowance, verify USDC transfer, USDC contract address, USDC on Solana, Solana USDC, check balance, SPL token, Associated Token Account, ATA, ERC-20 USDC, parseUnits, formatUnits, 6 decimals, viem, @solana/kit.
Teaches Vue state management with stores, Pinia, and the Composition API. Use when you need to share and synchronize state across multiple components beyond what props and events can handle.
Run technical UI quality checks across accessibility, performance, theming, responsiveness, and anti-patterns, then return a scored action plan.
TypeScript language expertise for type-safe, production-quality code. Use for advanced type system features (generics, discriminated unions, conditional and mapped types), strict mode configuration, type-safe APIs with zod/trpc/prisma, and modern tooling across Node, Deno, and Bun.
Analyze true Amazon profitability per ASIN and across a portfolio. Goes beyond per-unit fees to include advertising, returns, storage, promotions, and hidden fees, and finds where the real money is made and lost. Use when a user asks about profitability, true net profit, which products make money, why profit is lower than expected, portfolio profit analysis, or hidden fees eating margin. Trigger phrases: "profit analysis", "true profit", "net margin", "which products are profitable", "where is my money going", "hidden fees". Works with zero tools. the user pastes revenue, fee, and cost figures.
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.