Skill UI

An advanced workflow management skill designed for executing large-scale, multi-seed, or multi-stage Machine Learning experiments across remote GPU clusters via SSH. It manages complex dependencies, handles Out-of-Memory (OOM) retries, prevents race conditions between experimental 'waves,' and ensures robust, reproducible deep learning pipeline execution.

This skill provides a comprehensive guide and implementation for deploying a containerized Fathom AI meeting service. It is designed to handle the processing of meeting transcripts, summaries, and action items via webhooks. The solution includes Docker setup, environment variable management, robust health checks, and detailed steps for implementing rolling updates, ensuring continuous and reliable meeting data synchronization.

A foundational guide to interacting with the Fathom API. This skill demonstrates essential use cases, including listing meetings, fetching detailed transcripts, and retrieving AI-generated summaries and action items. It is ideal for initial API testing, validating connectivity, or understanding the Fathom data response structure.

A comprehensive guide for setting up and authenticating access to the Fathom AI API. This skill walks users through generating API keys, configuring environment variables, and testing connectivity to retrieve meeting transcripts, summaries, and action items. Essential for integrating Fathom's meeting intelligence features into custom applications.

This setup provides a local development loop for integrating and testing Fathom API functionalities. It utilizes mock meeting and transcript data, making it ideal for building meeting analytics tools, simulating webhook handlers, or iterating on complex transcript processing pipelines without requiring live API calls. It ensures a robust environment for isolated development testing.

This module provides robust handling for Fathom API rate limits (60 requests/minute per user). It implements advanced retry strategies (handling 429 errors and server failures) and efficient batch processing to reliably sync large volumes of meeting intelligence, such as transcripts and action items, into external systems like CRMs or project trackers.

This reference architecture provides a blueprint for integrating Fathom meeting intelligence data into enterprise systems. It details the full data pipeline, spanning from initial Fathom recordings via webhooks to structured storage (PostgreSQL), action item extraction, and downstream synchronization with CRMs (like Salesforce/HubSpot) and email systems. Ideal for developers building automated, real-time business process workflows.

Provides production-ready client patterns for interacting with the Fathom API, implemented in both Python and TypeScript. This SDK wrapper is designed for building reusable client services, implementing robust meeting data pipelines, or integrating Fathom's core features (like transcripts and summaries) into specialized applications.

This guide provides comprehensive strategies for managing version upgrades and handling breaking changes related to the Fathom AI meeting assistant API. It includes TypeScript examples for detecting schema drift, migrating old transcript formats to new speaker-attributed segments, and implementing robust rollback mechanisms, ensuring stable integration into downstream analytics and CRM systems.

A comprehensive tool for updating Feishu cloud documents. It supports seven distinct update modes including appending, full overwrite, targeted range replacement, and multi-instance replacement. The system allows highly precise positioning using content ranges (ellipsis) or chapter titles, ensuring content updates are localized and safe, thus preserving images, comments, and formatting.

A comprehensive guide to the three primary architecture patterns for integrating with Figma: CLI scripts, Webhook services, and dedicated plugins. This resource helps developers select the optimal pattern (REST vs. Plugin API) based on whether the use case requires real-time event handling, local automation, or in-editor component interaction. Ideal for planning robust Figma-connected applications like design token synchronization or linting.

Automate the critical workflows of design systems by integrating Figma directly into your CI/CD pipelines. This tool allows you to automatically sync design tokens on a schedule, export assets upon pull requests, and validate design system consistency, ensuring design-code parity and preventing manual errors during deployment.