threejs
claudeComplete Three.js skill set: fundamentals, geometry, materials, lighting, textures, animation, loaders, shaders, post-processing, and interaction.
10 skills
Model architecture design, training strategies, debugging, and paper interpretation (PyTorch/TensorFlow).
Plugin deep-learning-expert from marketplace ai-expert-skills in Miaoge-Ge/coding-agent-skills. Commands read from the repo's manifest — 1 month ago.
deep-learning-expertUse this skill when user needs to design deep learning models, perform training optimization, interpret papers, or solve engineering practice issues. Trigger keywords: deep learning, neural networks, model training, overfitting, gradient vanishing, PyTorch, TensorFlow, pretrained models, fine-tuning
Complete Three.js skill set: fundamentals, geometry, materials, lighting, textures, animation, loaders, shaders, post-processing, and interaction.
10 skills
Web accessibility (a11y): WCAG, semantic HTML, ARIA, keyboard nav, and screen-reader support.
1 skill
Designing REST and GraphQL APIs: resource modeling, versioning, pagination, errors, and contracts.
1 skill
Robust shell scripts: safe Bash, quoting, error handling, and portable POSIX patterns.
1 skill
Algorithmic problem solving, complexity analysis, and optimization for LeetCode/Codeforces/AtCoder.
1 skill
Modern C++ (17/20/23) expert for safe, performant code, templates, and UB/memory diagnosis.
1 skill
Build, train, evaluate, and use open source AI models, datasets, and spaces.
26 skills · 1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.
19 skills
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
1 skill · 3 commands · 1 agent
Data curation and processing at scale including NeMo Curator and Ray Data. Use when preparing training datasets or processing large-scale data.
98 skills
SkyPilot agent skill for launching cloud VMs, Kubernetes pods, and Slurm jobs across 25+ clouds
1 skill
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