Skill UI

Modal is a serverless cloud platform designed for executing Python-based AI/ML workloads. It provides on-demand access to powerful GPUs (e.g., H100, A100), scalable serverless functions, and custom containerization. Use cases include deploying ML models, running large-scale training/inference jobs, creating web APIs, and scheduling data pipelines, all defined entirely in code.

This skill provides robust mechanisms to interact with the Clari API, specifically designed to handle rate limiting (HTTP 429 errors) and manage asynchronous bulk export jobs. It implements exponential backoff and sequential scheduling, ensuring reliable data retrieval and forecasting exports without hitting API limits. Ideal for automated data pipelines and background job processing.

A comprehensive guide to diagnose and resolve common CoreWeave infrastructure errors. It covers issues related to GPU scheduling, Pod status (e.g., Pending), CUDA Out of Memory errors, NCCL timeouts, and Kubernetes resource allocation within AI/ML deployment pipelines.

A robust orchestration skill designed for managing large-scale, complex machine learning experiments on remote GPU clusters via SSH. It handles multi-seed sweeps, sequential wave transitions, and deep learning pipelines requiring state management. Features include OOM-aware automatic retries, cleanup of stale terminal sessions, and deterministic scheduling to prevent race conditions, making it ideal for grid computing and staged training.

This skill provides programmatic access to manage Hex workspace resources and configure scheduled project runs via the Hex Admin API. It allows users to automate repetitive tasks, monitor project execution status, and manage users, groups, and data connections using external cron triggers or API calls, ensuring robust workflow automation.

A comprehensive, production-ready reference architecture for building robust data scraping systems using Bright Data's full suite of tools (Web Unlocker, Scraping Browser, SERP API). This template provides best practices for project layout, managing proxies, designing data pipelines, and integrating scheduling/storage components in a Node.js/TypeScript environment.

This guide details the process of deploying production-grade inference services on CoreWeave using KServe. It covers setting up autoscaling, GPU scheduling, and implementing scale-to-zero functionality for efficient ML model serving endpoints. Ideal for MLOps engineers deploying large language models (LLMs) or other complex AI models in a production environment.

This comprehensive tool provides full calendar and scheduling management capabilities within the Feishu ecosystem. Users can create, retrieve, modify, and search for events, manage attendees (including users and meeting rooms), and check the free/busy status of multiple team members. It is essential for organizing complex meetings, coordinating team schedules, and ensuring smooth collaborative workflows.

Configures comprehensive CI/CD pipelines for Hootsuite social media management applications using GitHub Actions. It runs mock-based unit tests on every PR (validating scheduling and analytics) and performs live integration tests upon merging to the main branch, ensuring robust connectivity and reliable deployment.

This skill provides a comprehensive workflow for scheduling social media posts using the Hootsuite REST API. It manages the entire publishing lifecycle: users first upload media (images or videos) to obtain a media ID. Subsequently, the skill uses this ID, along with text content and target profiles, to schedule the post across various social networks. It supports both single and bulk scheduling operations, ensuring efficient content automation.

This bundle collects comprehensive diagnostic evidence for Hootsuite operations. It gathers API connectivity status, social profile health, scheduled post status, and OAuth token validity into a single, portable archive. Use it when troubleshooting persistent issues like failed scheduling, authentication errors, or disconnected social accounts.

This skill provides advanced techniques to optimize interactions with the Hootsuite API. It addresses common performance bottlenecks by implementing efficient LRU caching for social profiles, optimizing message scheduling through concurrent batch processing, and ensuring robust connection reuse. Use this when experiencing slow API responses or needing to process high volumes of automation tasks efficiently.