Skill UI

Automates Moonclerk tasks through the Composio Moonclerk toolkit by orchestrating Rube MCP calls, ensuring schema discovery, connection checks, and schema-compliant execution flows.

Automate Mx Toolbox operations through Composio’s Rube MCP by discovering tool schemas, managing the mx_toolbox connection, and issuing RUBE_MULTI_EXECUTE_TOOL calls with session-aware workflows for consistent, schema-compliant execution.

A comprehensive guide for developers migrating and upgrading Obsidian plugins across major API versions. It covers critical steps such as auditing plugin compatibility, implementing version-aware settings migration logic (for user data persistence), replacing deprecated API calls (e.g., Vault, Editor, FileManager), and updating configuration files like manifest.json to ensure stability on new Obsidian releases.

Automates Open Sea operations through Composio’s Open Sea toolkit by orchestrating Rube MCP calls, ensuring tool discovery, connection checks, and schema-compliant executions before running workflows.

Automates Openperplex operations by orchestrating Rube MCP calls via Composio, covering discovery, connection validation, tool execution, and schema-aware workflows.

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 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.

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.

This skill demonstrates advanced techniques for managing high-volume API calls to LLM services via OpenRouter. It implements multi-key round-robin strategies, incorporating health checks and circuit breakers to distribute load across multiple API keys. It also covers concurrent request processing using asyncio and provider-level failover, ensuring high availability and maximizing throughput when facing rate limits.

This skill guides developers on migrating their codebase from direct OpenAI API calls to using OpenRouter. By making minimal changes—primarily updating the base URL and API key—users gain access to over 400 models from multiple providers (including Anthropic and Google) through a unified SDK interface, ensuring high compatibility and flexible model switching.

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.

This skill implements a sophisticated, config-driven rules engine for OpenRouter API calls. It dynamically determines the optimal large language model (LLM) by evaluating a comprehensive context, including user subscription tier, remaining cost budget, specific task type (code, chat), required tools, and latency requirements. This is essential for building production-grade, cost-aware, and robust LLM orchestration systems.