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Found 55 Skills
Build minimal terminal emulators using the libghostty-vt C API with Raylib for windowing and rendering
Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.
Teaches how to interact with the Ray application. This skill should be used when users want to interact with Ray through a coding agent or LLM with skills capabilities.
Comprehensive GLSL shader techniques for creating stunning visual effects — ray marching, SDF modeling, fluid simulation, particle systems, procedural generation, lighting, post-processing, and more.
Deep technical understanding of pump.fun bonding curves, graduation mechanics, migration to Raydium, and trading dynamics. Use for building, analyzing, or trading pump.fun tokens.
Use when debugging a Nemo Gym run or reward profiling job. Covers rollout collection failures, empty or partial JSONL outputs, stale materialized inputs, verifier/schema errors, Ray or Slurm issues, vLLM readiness, judge failures, tool/sandbox failures, cache problems, and throughput bottlenecks.
Build raymarched space phenomena in Three.js. Use for black-hole lensing, accretion disks, wormholes, curved-ray integration, procedural star fields, relativistic-looking distortion, bounded volumetric structures, and GPU effects that need controlled numerical integration.
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
macOS launcher automation with Raycast extensions (TypeScript/React) and Alfred workflows (AppleScript/Python) for keyboard-driven productivity
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Use when user says "send to Ray," "show in Ray," "debug in Ray," "log to Ray," "display in Ray," or wants to visualize data, debug output, or show diagrams in the Ray desktop application.