Skill UI

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.

A comprehensive guide to minimizing API usage costs by implementing advanced integration patterns. Learn to reduce request volume through response caching with TTL, replacing expensive polling with webhook-driven sync, and utilizing smart polling with change detection filters. Also covers usage tracking and request deduplication for highly efficient system architecture.

This specialized skill provides a comprehensive framework for migrating applications using the MaintainX API across different versions (e.g., v1 to v2). It covers best practices for handling API breaking changes, deprecations, and version compatibility. Users can audit existing API usage, implement version adapters, and manage feature flag rollouts, ensuring smooth and safe integration upgrades.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

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.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.

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).

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 skill provides comprehensive, structured audit logging for OpenRouter API interactions. It automatically captures essential metadata like total cost, token usage, model details, latency, and status, while also performing PII redaction. The resulting audit trail is stored in an append-only SQLite database, making it ideal for building compliance-ready systems, detailed cost monitoring, and deep debugging of production environments.

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

This skill provides comprehensive tools for implementing granular cost controls for OpenRouter API usage. It allows users to set hard spending caps via per-key limits, track real-time credit balances, and implement client-side middleware for budget enforcement. Use cases include preventing unexpected overspending, managing multi-key environments, and optimizing API calls using cost-saving model variants.