Skill UI

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

Automates Esignatures IO operations through the Composio toolkit using Rube MCP, guiding users to discover tools, verify active connections, and execute compliant workflows with session-aware calls.

Automates Evenium operations through the Rube MCP and Composio toolkit by always discovering current tool schemas, validating Evenium connections, and executing schema-compliant workflows with proper session and pagination handling.

A comprehensive workflow for implementing advanced search and retrieval features within Evernote. It allows users to build complex queries using search grammar, filter notes by tags, notebooks, dates, and completion status, and paginates through large result sets. The service also enriches results by mapping GUIDs to human-readable names for notebooks and tags.

Generates Excalidraw diagrams from natural-language prompts, turning requests such as “make a flowchart” or “draw a system architecture” into ready-to-open .excalidraw JSON files for flows, relationships, mind maps, and architectures.

Automates Expofp workflows by orchestrating Composio’s toolkit via the Rube MCP interface, guiding you through discovering current schemas, validating connections, and invoking multi-tool executions with session-aware coordination.

Automate Fireberry operations via Composio’s toolkit by discovering Rube MCP tools, managing connections, and executing schema-driven workflows while enforcing session, memory, and pagination practices.

This comprehensive guide details how to set up a robust, local development environment for Firecrawl integrations. It utilizes self-hosted Docker containers and Redis to eliminate reliance on paid API credits during development. The setup supports both unit testing (with mocked SDK) and full integration testing against the local instance, ensuring a seamless and efficient workflow for scraping and web data extraction projects.

A comprehensive guide for migrating web scraping pipelines from tools like Puppeteer, Playwright, and Cheerio to Firecrawl. This skill demonstrates how to replace complex, browser-intensive code with simple API calls, handling single-page scraping, structured data extraction using LLMs, and large-scale site crawling, while eliminating the need for manual browser management and anti-bot workarounds.

This guide outlines essential security best practices for integrating Firecrawl. It covers secure API key storage (using environment variables and .gitignore), verifying webhook signatures with HMAC-SHA256 for data integrity, implementing key rotation strategies across different environments (dev/staging/prod), and sanitizing scraped content to mitigate XSS and PII risks. Essential for building secure, production-grade systems.

A comprehensive guide for deploying Fireflies.ai integrations into production environments. This skill covers setting up the GraphQL client, implementing secure webhook receivers with signature verification, and managing multi-platform deployments across Vercel, Docker, and Google Cloud Run. Ideal for integrating AI-powered meetings data into production services.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.