torch-geometric

21.8k

davila7

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

pyhealth

21.8k

davila7

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

histolab

21.8k

davila7

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

shap

21.8k

davila7

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

scanpy

10.8k

K-Dense-AI

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.

pyhealth

10.8k

K-Dense-AI

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

pytorch-lightning

10.8k

K-Dense-AI

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

histolab

10.8k

K-Dense-AI

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

shap

10.8k

K-Dense-AI

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

torch-geometric

10.8k

K-Dense-AI

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

scvi-tools

8.4k

anthropics

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

optimizing-deep-learning-models

1.5k

Jeremylongshore Claude Code Plugins Plus Skills Deep Learning Optimizer

This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".

domain-ml

786

actionbook

Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, machine learning, artificial intelligence, model inference

Object Detection/Segmentation Skill

376

a5c-ai

Deep learning based object detection and segmentation for robotics applications

bio-imaging-mass-cytometry-cell-segmentation

293

GPTomics

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

bio-variant-calling-deepvariant

293

GPTomics

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.

bio-long-read-sequencing-clair3-variants

293

GPTomics

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

medical-imaging-review

238

luwill

Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.

sam-cell-seg

62

letta-ai

This skill provides guidance for tasks involving MobileSAM or Segment Anything Model (SAM) for cell segmentation, mask refinement, and polygon extraction from images. Use when working with SAM-based image segmentation pipelines, converting masks to polygons, processing CSV-based coordinate data, or integrating deep learning segmentation models into processing scripts.

caffe-cifar-10

62

letta-ai

Guidance for building and training with the Caffe deep learning framework on CIFAR-10 dataset. This skill applies when tasks involve compiling Caffe from source, training convolutional neural networks on image classification datasets, or working with legacy deep learning frameworks that have compatibility issues with modern systems.

typst-paper

49

bahayonghang

Typst academic paper assistant (Chinese and English papers for conference or journal submissions). Use when writing, reviewing, compiling, or improving Typst academic papers. Use when the user mentions typst compile, grammar, bibliography, deai, translate, title, logic, reviewer perspective, or any Typst paper quality improvement task. Domains: Deep Learning, Time Series, Industrial Control, Computer Science.

latex-paper-en

49

bahayonghang

LaTeX academic paper assistant for English papers (IEEE, ACM, Springer, NeurIPS, ICML). Use when writing, reviewing, compiling, or improving English LaTeX academic papers. Use when user mentions compile, grammar, bibliography, deai, translate, title, logic, reviewer perspective, or any LaTeX paper quality improvement task. Domains: Deep Learning, Time Series, Industrial Control.

feynman

47

neurofoo

Feynman Technique for deep learning—explain a concept simply, identify gaps, fill them, then refine. Use when learning something new, testing understanding, or preparing to teach.

langchain_patterns

39

vuralserhat86

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

model_finetuning

39

vuralserhat86

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

ml-engineer

36

zenobi-us

Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization. Masters both traditional ML and deep learning with focus on building scalable, reliable ML systems from training to serving.

ai-ml-timeseries

34

vasilyu1983

Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.

pyhealth

26

lifangda

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

aeon

26

lifangda

Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices.

torch-geometric

26

lifangda

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

histolab

26

lifangda

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

shap

26

lifangda

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

ai-ml-engineer

25

nahisaho

Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learning Use when: User requests involve AI/ML engineer tasks.

deep-learning

23

Mindrally

Comprehensive deep learning guidelines for neural network development, training, and optimization.

holomorphic-dynamics

23

Tibsfox

Educational pack for holomorphic dynamics, complex iteration, fractal geometry, and data-driven dynamics (DMD/Koopman). Use this skill when the user asks about: complex dynamics, iteration on the complex plane, Julia sets, Mandelbrot sets, fixed points and stability, period doubling, bifurcation, topology of the complex plane, DMD (Dynamic Mode Decomposition), Koopman operator theory, skill dynamics as a dynamical system, fractal rendering, escape-time algorithms, or connections between dynamics and deep learning.

computer-vision-opencv

23

Mindrally

Expert guidance for computer vision development using OpenCV, PyTorch, and modern deep learning techniques for image and video processing.

deep-learning-pytorch

23

Mindrally

Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.

pytorch

23

Mindrally

PyTorch deep learning development with transformers, diffusion models, and GPU optimization.

deep-learning-python

23

Mindrally

Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.

agent-ml-engineer

15

Tony363

Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization. Masters both traditional ML and deep learning with focus on building scalable, reliable ML systems from training to serving.

histolab

15

Oimiragieo Agent Studio Histolab

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

shap

15

oimiragieo

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

scanpy

15

oimiragieo

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.

pyhealth

15

Oimiragieo Agent Studio Pyhealth

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

pytorch-lightning

15

oimiragieo

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

torch-geometric

15

Oimiragieo Agent Studio Torch Geometric

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

pytorch

9

TerminalSkills

Assists with building, training, and deploying neural networks using PyTorch. Use when designing architectures for computer vision, NLP, or tabular data, optimizing training with mixed precision and distributed strategies, or exporting models for production inference. Trigger words: pytorch, torch, neural network, deep learning, training loop, cuda.

optimizing-deep-learning-models

8

BbgnsurfTech

Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance".