Skill UI

This guide establishes a comprehensive and rapid development loop for Obsidian plugins. It covers the entire workflow, from setup (cloning templates, symlinking) to advanced features, including running esbuild in watch mode, achieving hot-reloading (via Ctrl+R or automatic methods), deep debugging with Chrome DevTools, and running unit tests with vitest. Essential for developers seeking fast feedback and reliable plugin iteration.

This deep-dive tool automates the complex process of migrating notes and knowledge bases from various platforms (such as Notion, Evernote, Roam, etc.) into Obsidian. It handles critical conversions like stripping platform-specific UUIDs, converting internal links to optimized Obsidian wikilinks ([[...]]), and correctly formatting attachments and YAML frontmatter, ensuring a seamless transition and preserving the structural integrity of your knowledge base.

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.

A comprehensive guide to the Obsidian event system, detailing how to handle various lifecycle and state changes within plugins. Learn to register listeners for vault events (file creation, modification, deletion), workspace events (layout changes, view activation), and metadata cache updates. Essential for building highly reactive and deeply integrated Obsidian plugins.

Automate Onedesk workflows via Composio’s Rube MCP by discovering the latest tool schemas, managing connections, and invoking toolkit actions with schema-compliant arguments, session reuse, and mandatory tool discovery for each run.

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 deep dive guide provides comprehensive strategies and detailed checklists for migrating to the OpenEvidence platform. It covers various migration methods, such as parallel run, Strangler fig, and Big bang approaches. The guide assists technical teams in planning the transition by addressing key areas including API mapping, data migration plans, rollback procedures, and performance baselines, ensuring a secure and smooth transition for healthcare IT systems.

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 strategies for implementing robust caching mechanisms for OpenRouter API calls. It covers in-memory caching using Python, persistent caching with Redis for scalability, and utilizing provider-specific features like Anthropic prompt caching. By implementing deterministic key generation and managing Time-To-Live (TTL), developers can significantly reduce API costs and improve application latency when handling repetitive or similar LLM queries, making it ideal for RAG systems and high-volume applications.

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.

Implements standardized function/tool calling across various large language models (LLMs) via OpenRouter. This unified approach allows developers to build sophisticated, multi-step agents and complex workflows, regardless of the underlying provider (e.g., GPT-4o, Claude 3.5). It simplifies the process of achieving structured output and tool-augmented reasoning in production systems.

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.