Skill UI

An AI communication style that minimizes token usage while maintaining technical accuracy. It removes filler words, articles, and hedging, adhering to Simplified Technical English principles. Offers intensity levels from lite to ultra and classical Chinese variants for maximum brevity and clarity in technical contexts.

Compresses natural-language memory files such as CLAUDE.md, todo lists, and preferences into terse caveman-style text to reduce input token usage. Preserves code blocks, inline code, URLs, headings, and important technical terms while backing up the original to an out-of-tree data directory.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.

Generate production-quality React components and UIs using the specialized Rayden UI component library and Tailwind CSS. This skill ensures that all generated code strictly adheres to a defined design system, including correct component usage, design tokens, prop validation, and established layout patterns. Ideal for scaffolding complex pages like dashboards, login forms, and data tables while maintaining premium aesthetics and consistency.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.

A logging wrapper designed to instrument and monitor calls to the Claude API. It automatically tracks critical metrics including token usage, API latency, estimated cost, and error details. Essential for implementing robust observability patterns and setting up proactive alerts in production environments.

This module provides comprehensive observability for Anthropic's Claude API integrations. It enables the tracking of critical operational metrics including latency, token usage (input/output), estimated API costs, and error rates. Use it to set up structured logging and Prometheus metrics for robust monitoring and alerting, ensuring reliable and cost-effective AI application deployments.

This skill provides robust strategies for managing AppFolio API rate limits. It implements token bucket throttling, exponential backoff, and comprehensive retry logic to ensure reliable execution of high-volume operations, such as bulk tenant imports, rent-roll syncs, and work-order batch updates. It prevents API quota exhaustion during peak usage times.

A comprehensive guide to securing ClickUp API usage. Learn best practices for managing API tokens, implementing least-privilege access, and auditing usage patterns. This includes secure token rotation procedures, using environment variables, setting up pre-commit hooks to prevent leaks, and structuring calls for robust audit logging.

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.

A comprehensive solution for setting up production-grade observability for Cohere API v2. It integrates Prometheus metrics, OpenTelemetry tracing, and robust alerting mechanisms. This skill tracks critical operational data including per-endpoint latency, token consumption (input/output), error rates, and usage costs, ensuring reliable monitoring for AI-powered applications.