Skill UI

This skill is designed to generate high-quality UX microcopy, including button labels, error messages, empty states, and toast notifications. It ensures the copy maintains a casual but polite, active, and concise tone, adhering to modern best practices in UX writing and design language guidelines.

This skill provides templates and guidelines for conducting code reviews in Chinese-speaking teams. It helps balance feedback tone, avoiding being too polite or too blunt. It includes priority labeling (must fix, suggest, reference), mixed Chinese-English comment standards, and commit message formats. It also addresses common anti-patterns like excessive politeness or style wars, ensuring code quality without damaging team morale.

PyTorch Geometric is a standard library for Graph Neural Networks built on PyTorch. It supports node, link, and graph classification, message passing algorithms like GCN and GAT, heterogeneous graphs, and neighbor sampling. Ideal for building scalable GNN models with custom datasets and transforms.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide providing minimal, working examples for integrating with the Anthropic Claude Messages API. Covers core functionalities including basic text completion, advanced vision capabilities for image analysis, and real-time streaming responses. Ideal for developers starting a new Claude integration or testing core API patterns.

This guide teaches developers how to implement robust, event-driven architecture patterns with the Anthropic Claude API. It covers two key methods: Server-Sent Events (SSE) for real-time streaming responses, and the Message Batches API for high-volume, asynchronous bulk processing. Use this when building real-time chat features or processing large datasets offline.

This skill integrates the Clawdbot agent with the LINE messaging platform, enabling robust communication capabilities. It supports core messaging actions such as sending text, sending files, and replying to user messages. This is highly useful for developing AI agents and bots marketed to regions like Japan, Taiwan, Thailand, and Indonesia.

This skill integrates an AI agent (Clawdbot) with Signal, one of the most private messaging platforms. It enables automated communication by utilizing the signal-cli tool to perform actions such as sending messages, sending files, reacting, and replying within a designated chat channel. Ideal for building secure, automated bot interactions in private messaging environments.

A comprehensive guide to minimizing expenses when utilizing the Anthropic Claude API. Learn advanced strategies including right-sizing models (Haiku, Sonnet, Opus), prompt caching (up to 90% savings), message batching, and effective token reduction techniques. Essential for building cost-efficient AI applications.

A comprehensive guide for scaling Claude usage in high-throughput, enterprise-level applications. Learn four advanced strategies, including Message Batches for bulk processing, using p-limit for concurrency control, managing rate limit tier upgrades, and selecting the optimal model (e.g., Haiku) to balance speed and quality.

This guide covers advanced usage of the Anthropic Messages API. Learn how to implement real-time streaming responses, utilize system prompts for persona control, process visual inputs (Vision), and enforce structured data output (JSON). This is essential for building sophisticated, robust, and multi-modal Claude-powered applications.