Skill UI

This toolkit provides a comprehensive framework for managing and optimizing API costs when using Mistral AI. It includes functions for calculating token-based costs across various models, implementing a smart model router based on task complexity, and managing daily/monthly budgets with critical alerts. Furthermore, it offers methods for prompt optimization to reduce token consumption, making it essential for large-scale application cost control and billing analysis.

This skill provides comprehensive patterns for handling sensitive data when integrating with Mistral AI. It covers PII redaction (emails, phone numbers, etc.), implementing data retention policies, and enforcing compliance with global privacy regulations like GDPR and CCPA. Key features include safe API wrappers, dataset sanitization for fine-tuning, and conversation history management with TTL and user erasure rights.

This guide provides advanced techniques for optimizing Mistral AI API usage, focusing on latency reduction and throughput maximization. Learn to implement robust caching strategies, utilize streaming responses (SSE), select the optimal model for specific use cases (e.g., Mistral Small for chat, Codestral for code), manage concurrent requests using rate limiting, and efficiently use the Batch API. Essential for building high-performance, production-grade AI applications.

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

A comprehensive guide to building modern, high-performance full-stack applications using Next.js 14+ and the App Router. Covers advanced concepts including React Server Components (RSC), Server Actions, optimized data fetching with caching, implementing robust SEO via generateMetadata, and best practices for deployment and performance tuning (Vercel).

A comprehensive guide to managing and reducing costs associated with Obsidian's paid services (Sync, Publish) and external API calls from community plugins. Learn how to audit vault size, implement secure exclusion patterns for sync, and utilize coding techniques like caching and rate limiting to control usage fees from external services (e.g., AI).

A comprehensive guide for optimizing Obsidian plugin performance, especially when dealing with large knowledge vaults (10,000+ files). Learn advanced techniques such as profiling bottlenecks with DevTools, implementing lazy initialization to speed up startup, processing files in manageable batches with UI yielding, and utilizing LRU caches for bounded memory management. Essential for creating stable, high-performance plugins.

This skill provides comprehensive guidelines and optimization strategies for managing API usage and reducing costs when interacting with OpenEvidence. It covers best practices such as implementing caching, batching requests, selecting appropriate API tiers, and monitoring usage dashboards to ensure efficient and cost-effective data retrieval in healthcare applications.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

This skill provides comprehensive best practices and Python code examples for optimizing API calls to Large Language Models (LLMs) via OpenRouter. It covers benchmarking model latency, implementing streaming to achieve low Time-To-First-Token (TTFT), and processing requests concurrently using asynchronous programming (asyncio). Use this when developing real-time, high-throughput applications that require predictable and fast model responses.

Guides the user through configuring and tuning database connection pools across languages, from selecting pool sizes to setting timeouts and monitoring usage to boost application performance.

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.