Skill UI

A comprehensive, production-ready toolkit for robustly debugging and monitoring Linear API integrations. This bundle includes features like an instrumented client wrapper for detailed request/response logging, a performance request tracer with summary statistics (avg, p95, max latency), a health check utility, and an environment validator. Ideal for developers setting up logging, performance monitoring, or debugging utilities in a live SaaS environment.

This skill enforces mandatory code quality assurance by executing industry-standard validation tools before any code is considered complete or committed. It covers linting (style and syntax checking), type checking (for TypeScript and Python), and security auditing (e.g., npm audit, bandit). It ensures developers maintain a high standard of code reliability across diverse ecosystems like Node.js and Python.

This comprehensive guide provides production-ready patterns for building advanced LLM applications. It details the entire RAG pipeline, covering document ingestion, various chunking strategies, and advanced retrieval methods (Hybrid, Multi-query). Furthermore, it covers sophisticated agent architectures, such as ReAct, enabling complex reasoning and tool use. Ideal for developers designing enterprise-grade AI systems.

Guides developers on automating Lmnt operations via the Composio Lmnt toolkit using Rube MCP, emphasizing mandatory tool discovery, connection checks, schema compliance, and workflow session management before executing multi-tool workflows.

A comprehensive workflow tool for managing the entire localization process using Lokalise. It handles downloading translated files (JSON, XLIFF, PO), reviewing translation status, updating keys, and integrating the output directly into development applications. Ideal for CI/CD pipelines and developers needing to sync localized content efficiently.

This comprehensive SDK provides tools for building enterprise-grade AI agents integrated across the Microsoft 365 ecosystem, including Teams and Copilot Studio. It supports advanced features such as Express hosting, structured routing, streaming responses via Azure OpenAI, and direct client integration, enabling developers to build sophisticated business automation solutions.

Used for building, debugging, and extending MCP servers and clients. This skill implements robust connections between AI systems and external tools/data sources using structured protocols like JSON-RPC 2.0. Expertise includes defining tool schemas (Zod/Pydantic), configuring transport layers (HTTP/SSE/stdio), and scaffolding full-stack projects in TypeScript or Python.

Guides plugin developers through integrating Model Context Protocol (MCP) servers into Claude Code, covering configuration files, server types (stdio, SSE, HTTP, WS), environment variables, and authentication so external services appear as structured tools.

This tool gathers comprehensive diagnostic evidence from an Obsidian vault. It collects details such as app versions, installed plugins, appearance settings, file statistics, and console errors into a structured markdown report. Use it when troubleshooting persistent issues or preparing detailed bug reports for developer support.

This runbook provides systematic procedures for diagnosing and resolving critical failures within the Obsidian knowledge base. It guides users through recovering from plugin crashes, vault corruption, sync conflicts, and performance degradation using advanced command-line tools and developer console access. Essential for advanced users and technical support.

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 provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.