Machine Learning AI Agent Skills

Build comprehensive ML pipelines, experiment tracking, and model

Build production ML systems with PyTorch 2.x, TensorFlow, and

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deploy

Design and implement a complete ML pipeline for: $ARGUMENTS

Screenpipe brand style guide. Reference this when designing UI components, writing copy, or making visual decisions.

MCP server for spatial transcriptomics analysis via natural language Using Claude Code / Codex / OpenCode? Just paste this: Manual installation See Installation Guide for detailed setup including virtual environments and all MCP clients. 60+ methods across 15 categories. Supports 10x Visium, Xenium, Slide-seq v2, MERFISH, seqFISH. MIT License · GitHub · Issues

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.

Welcome to the awesome-ai-research-writing project! This application helps you elevate your AI research writing without the usual hassle. Let’s guide you through the steps to download and run the application smoothly. 1. Visit the Releases page to download your copy of the software. 2. Choose the latest version available. Look for the asset that fits your operating system. 3. Click the download li

Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.

   Give AI assistants (Claude, Cursor, VS Code, etc.) the ability to control your desktop and automate tasks across any application. Claude Code (one-liner): Other clients (Cursor, VS Code, Windsurf, etc.): Add to your MCP config file: See the MCP Agent README for detailed setup instructions. - Uses your browser session - no need to relogin, keeps all your cookies and auth - Doesn't take

👋 join us on Discord and WeChat If you like Dingo, please give us a ⭐ on GitHub! Dingo is A Comprehensive AI Data, Model and Application Quality Evaluation Tool, designed for ML practitioners, data engineers, and AI researchers. It helps you systematically assess and improve the quality of training data, fine-tuning datasets, and production AI systems. 🎯 Production-Grade Qua