Automation skills

This skill analyzes a codebase and suggests Claude Code automations across hooks, subagents, skills, plugins, and MCP servers to optimize setup.

This skill enforces runtime safety for LLMs with configurable jailbreaking, toxicity, PII, and fact-checking rails to improve reliability.

This skill helps you generate music or sounds from text descriptions using AudioCraft, enabling melody-conditioned and stereo audio output.

This skill helps you generate high-quality images from text prompts, perform image-to-image tasks, and optimize diffusion workflows with Stable Diffusion.

This skill guides fine-tuning LLMs with TRL for instruction tuning, preference alignment, and reward-based optimization, aligning models to human feedback.

This skill helps orchestrate ML workloads across multiple clouds with automatic cost optimization and spot instance recovery.

This skill optimizes LLM data curation with GPU-accelerated, multi-modal cleaning, deduplication, and PII redaction to improve training data quality.

This skill helps you streamline PyTorch Lightning training, automate distributed execution, and reduce boilerplate for scalable, reproducible experiments.

This skill helps you align AI safety using self-critique and AI feedback, reducing harmful outputs without human labeling.

This skill provides expert guidance for fast fine-tuning with Unsloth, enabling 2-5x training speed and reduced memory usage.

This skill helps you build complex AI systems with declarative LM programming, automatic prompt optimization and modular RAG pipelines for reliable outputs.

This skill extracts and validates structured data from LLM responses using Pydantic, with automatic retries and real-time streaming.

This skill simplifies distributed training with HuggingFace Accelerate, enabling seamless multi-GPU/TPU setups via a four-line integration.

This skill helps you manage Lambda Labs GPU Cloud resources for scalable ML training and inference with persistent storage and easy SSH access.

This skill fine-tunes and evaluates OpenVLA-OFT policies for robot action generation with LoRA and FiLM conditioning.

This skill automates end-to-end AI research projects by managing loops, literature search, experiments, and synthesis to guide direction and produce papers.

This skill guides enterprise RL training with miles for large MoE models, enabling FP8/INT4, train-inference alignment, and speculative RL for throughput.

This skill enables scalable pretraining of large language models using PyTorch Torchtitan 4D parallelism across GPUs, delivering faster training with efficient

This skill helps you benchmark LLMs across 100+ benchmarks with containerized, scalable evaluation on local Docker, Slurm HPC, or cloud platforms.

This skill helps you track ML experiments, visualize training, sweep hyperparameters, and manage models using Weights & Biases for streamlined MLOps.

This skill helps you implement and train LLMs with LitGPT across 20+ pretrained architectures for clean, production-ready workflows.