Skill UI

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

This skill provides comprehensive guidance on deploying and tuning Web Application Firewall (WAF) rules across major cloud platforms, including AWS, Azure, and Cloudflare. It details strategies to protect cloud-hosted applications against OWASP Top 10 attacks, implement rate limiting for brute force protection, configure geo-blocking, and refine rules to minimize false positives.

Guides fine-tuning and evaluation of OpenVLA-OFT/OFT+ for robot action generation, covering LoRA adaptation, continuous action heads, FiLM conditioning, and LIBERO/ALOHA benchmarks plus deployment considerations.

Provides end-to-end guidance for fine-tuning and serving Physical Intelligence’s OpenPI pi0 family (pi0, pi0-fast, pi0.5) with both JAX and PyTorch backends, covering dataset prep, norm stats, checkpoint conversion, and low-latency WebSocket policy inference for ALOHA, DROID, and LIBERO tasks.

This skill provides a managed cloud environment for training and fine-tuning advanced vision models. Users can build and customize models for object detection (e.g., DETR, YOLOS), image classification, and segmentation (SAM/SAM2). By leveraging Hugging Face Jobs, training is executed on powerful cloud GPUs without requiring local GPU setup, and all resulting models are automatically saved to the Hugging Face Hub.

This skill guides the setup and execution of GPU-intensive Machine Learning workloads on Modal, a serverless cloud platform. It details a zero-config workflow for training, fine-tuning, and inference, eliminating the need for manual SSH setup or complex Docker orchestration. Key steps include detailed cost estimation, selecting the optimal GPU based on VRAM requirements, and generating reproducible Python launcher scripts for reliable ML experimentation.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

This comprehensive guide provides advanced techniques to diagnose and significantly boost ClickHouse query performance. It covers analyzing slow queries using system tables, optimizing ORDER BY keys, implementing data skipping indexes (Bloom, Set, MinMax), utilizing projections for pre-aggregation, and fine-tuning server settings and materialized views. Essential for high-volume OLAP environments.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

This comprehensive skill manages the entire computer vision pipeline, from custom dataset annotation to model deployment. Users can fine-tune YOLO models on proprietary COCO-formatted datasets using an agent-driven workflow. Key features include hardware-aware training (supporting CUDA, MPS, ROCm), automatic export to optimized formats (TensorRT, CoreML, OpenVINO, ONNX), and seamless, one-click deployment as an active detection skill.

A comprehensive reference guide for common errors encountered when interacting with the Together AI API. This includes error codes and solutions for inference, fine-tuning, and model deployment, covering authentication, rate limits, validation failures, and billing issues. Essential for developers integrating OpenAI-compatible LLM services.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.