Skill UI

A comprehensive guide to optimizing Groq API usage and managing inference costs. Learn techniques such as smart model routing (selecting smaller models for simple tasks), minimizing prompt tokens, batching requests to reduce overhead, and implementing caching for deterministic calls. This guide helps developers achieve significant cost savings and improve system efficiency when handling high-volume AI workloads.

A comprehensive, compliant wrapper for integrating with the Groq API. This module automatically sanitizes input prompts and filters model responses to redact sensitive PII (emails, phone numbers, SSN, etc.). It also tracks detailed token usage and generates robust audit logs, ensuring data privacy and regulatory compliance (GDPR, etc.) in enterprise AI applications.

This guide details how to implement robust, multi-layered Role-Based Access Control (RBAC) for managing LLM API calls to Groq in an enterprise setting. It covers best practices for API key scoping, application-level authorization (model selection, token limits), rate limiting, and detailed spending control mechanisms, ensuring secure and cost-effective usage across multiple teams.

A comprehensive guide to optimizing LLM API usage within LangChain frameworks. Techniques include implementing token usage tracking, model tiering to route tasks based on complexity, utilizing caching to eliminate duplicate calls, prompt compression for efficiency, and enforcing strict budget limits to manage operational costs effectively.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

This comprehensive guide details advanced strategies for building highly resilient applications that interact with external LLM APIs. It covers built-in exponential backoff retries, controlling request throughput via concurrency limits, implementing provider fallbacks, and custom rate limiting using token buckets. Essential knowledge for achieving stable, high-volume API usage in production systems.

This bundle provides a structured mechanism for capturing comprehensive debug information when interacting with the OpenRouter API. It captures the full request parameters, response content, detailed error logs, generation metadata (such as cost, token usage, and provider), and the execution environment. This tool is essential for diagnosing failed API calls, unexpected behaviors, or performance/latency issues.

A specialized audit skill designed to identify and quantify resource leakage, excessive costs, and billing vulnerabilities within complex development tool workflows (e.g., GitHub Apps). It traces webhook events, queue processing, and model calls to pinpoint where token burn or unauthorized resource usage is occurring, ensuring cost efficiency and clean billing practices.

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 comprehensive reference guide and toolkit for stylistic text compression. It provides multiple modes (Lite, Full, Ultra) and specialized skills (e.g., commit messages, review comments) to transform verbose, chatty text into extremely concise, professional prose. Ideal for developers needing to write terse commit logs, academic summaries, or simply minimize token usage.