ML Ops
Machine learning operations
torch-sim Skill
by fl-sean03
data-analysis skill - Analyzing torch-sim outputs
product-frameworks-for-design-and-management
by kjgarza
Comprehensive product design and management frameworks including UX heuristics (Hick's Law, Fitts's Law), design processes (Double Diamond, Lean UX, Agile, User-Centered Design), prioritization methods (RICE, MoSCoW, Kano Model, Pareto Principle), product lifecycle guidance, PRD templates, user story formats, design critique guidelines, and usability testing checklists. Use when analyzing features, creating product documentation, facilitating design reviews, planning user research, applying UX principles to interfaces, or making product decisions that require framework-based guidance.
aio-mental-models
by aiocean
This skill should be used when the user faces complex decisions, problem-solving, debugging, system design, strategic thinking, or needs structured reasoning. Comprehensive mental models framework with 50+ models covering first principles, second-order thinking, inversion, feedback loops, and more.
graphics-rendering
by pluginagentmarketplace
3D graphics, shaders, VFX, lighting, rendering optimization. Create stunning visuals with production-ready techniques.
veo3-prompter
by leegonzales
Craft professional video prompts for Google Veo 3.1 using cinematic techniques, audio direction, and timestamp choreography. Use when generating AI videos, creating video prompts, or working with Veo 3.
AI Safety Auditor
by jmsktm
Audit AI systems for safety, bias, and responsible deployment
cost-modeling
by LunCoSim
Perform parametric cost estimation and ROM (Rough Order of Magnitude) costing for space missions. Use this skill to generate lifecycle cost estimates using CERs, compare launch costs, estimate operations budgets, and support cost-driven trade studies. Trigger this for "cost estimate," "ROM cost," "mission cost," "launch cost," "cost model," "lifecycle cost," "operations cost," or whenever a trade study needs cost as a Figure of Merit.
architecture-serverless
by KentoShimizu
"Serverless architecture workflow for event-driven and bursty workloads using managed compute and platform services. Use when elasticity and reduced platform operations justify managed-service constraints; do not use when workload shape requires long-lived stateful control loops."
cost-estimation
by LunCoSim
Perform parametric cost estimation and cost-risk analysis for space missions. Use this skill to generate ROM (Rough Order of Magnitude) costs, build Cost Estimating Relationships (CERs), produce cost breakdowns by subsystem and WBS element, and generate cost S-curves for risk assessment. Trigger this for "mission cost," "cost estimate," "ROM cost," "cost model," "cost-to-complete," "budget estimate," "proposal cost," "cost breakdown," or "cost risk."
design-qa-implementation-parity
by KentoShimizu
"Verify implementation parity against approved design specs with severity-based decisions and fix guidance. Use when implemented UI must be compared against approved specs before release or sign-off; do not use for backend data-model or deployment pipeline decisions."
Computer Vision Helper
by jmsktm
Assist with image analysis, object detection, and visual AI tasks
django-app-development
by KentoShimizu
"Django application development workflow for production-grade app changes. Use when Django-specific code (models, views, serializers/forms, routing, settings, middleware, auth flows) must be implemented or revised with framework runtime behavior in scope; do not use for repository-wide architecture governance or release management policy."
Business Plan Writer
by jmsktm
Create comprehensive business plans, financial projections, and strategic documents for funding or planning
data-structures
by KentoShimizu
"Select data structures using explicit access patterns, mutation behavior, memory limits, and concurrency constraints. Use when implementation correctness or performance depends on choosing between alternatives (array/map/heap/tree/queue/set) under real workload assumptions; do not use for persistence schema or deployment topology decisions."
cnn-vision
by levy-n
Implements CNN architectures for computer vision tasks. Covers convolution operations, pooling, CNN design patterns (LeNet, ResNet, VGG), transfer learning, fine-tuning pretrained models, data augmentation, and image preprocessing. Use when building image classifiers, doing object detection, or when user mentions 'CNN', 'convolution', 'pooling', 'ResNet', 'VGG', 'transfer learning', 'fine-tuning', 'image augmentation', 'ImageNet', 'feature maps', 'MNIST', 'image classification', 'multi-modal', 'image captioning', or 'multimodal network'.
deep-learning-core
by levy-n
Explains neural network fundamentals: the Three Pillars (Model, Loss, Optimizer), backpropagation, gradient descent variants (SGD, Adam), regularization (Dropout, BatchNorm), and MLP architecture design. Use when learning how neural networks work, debugging training issues, or when user asks about 'backpropagation', 'vanishing gradients', 'learning rate', 'loss function', 'overfitting', 'underfitting', 'activation functions', 'why isn\'t my model learning', 'gradient descent', 'Adam', 'Dropout', 'BatchNorm', 'autoencoder', 'denoising autoencoder', or 'latent space'.
gemini
by G1Joshi
Google Gemini AI models for multimodal tasks. Use for multimodal AI.
ml-knowledge-index
by levy-n
Routes ML/DL questions to specialized skills. Use FIRST when unsure which skill applies, when user asks broad ML questions, or when multiple topics might be relevant. Maps: regression/classification → ml-fundamentals, ensembles/clustering → ml-advanced, TF-IDF/Word2Vec → nlp-classical, training/backprop → deep-learning-core, PyTorch → pytorch-mastery, CNNs/images → cnn-vision, LSTM/time-series → sequence-models, BERT/HuggingFace → transformers-llm, RAG/embeddings → rag-retrieval, APIs/PDF-parsing → data-pipeline, LoRA/QLoRA/PEFT → fine-tuning-peft, MLflow/W&B/Optuna → mlops-experiment, SHAP/Grad-CAM → model-interpretability, Q-learning/PPO/DQN → reinforcement-learning, GAN/VAE/diffusion → generative-models, explanations → ml-teaching-assistant.
agentic-ui-contract
by phrazzld
Design and implement agentic product flows using the contract: model decides what to do, tools decide how it is done, UI schema decides how it is rendered. Use for chat-first apps, tool-calling agents, generative UI systems, and planner/tool architecture decisions. Keywords: agentic UX, tool calling, planner, generative UI, function tools.
ml-fundamentals
by levy-n
Implements classical ML algorithms for regression and classification. Covers Linear/Polynomial/Logistic Regression, Decision Trees, Ridge/Lasso regularization, train/test splits, cross-validation, and evaluation metrics (R², RMSE, Precision, Recall, F1, ROC-AUC, Confusion Matrix). Use when building predictive models on tabular data, comparing baseline algorithms, handling imbalanced data, or when user mentions 'regression', 'classification', 'overfitting', 'cross-validation', 'confusion matrix', 'feature importance', 'precision/recall', or 'regularization'.
huggingface
by G1Joshi
Hugging Face transformers library and hub. Use for NLP models.
nlp-classical
by levy-n
Implements traditional NLP techniques before transformers. Covers text vectorization (TF-IDF, Bag-of-Words), word embeddings (Word2Vec, FastText, GloVe, Doc2Vec), topic modeling (LDA, Gensim), and text similarity (Jaccard, Cosine, FuzzyWuzzy, record linkage). Use when building text classifiers without deep learning, doing topic extraction, entity matching, or when user mentions 'TF-IDF', 'Word2Vec', 'topic modeling', 'LDA', 'text similarity', 'n-grams', 'document clustering', 'GloVe', 'Doc2Vec', 'FuzzyWuzzy', or 'record linkage'.
claude
by G1Joshi
Anthropic Claude AI models for analysis and coding. Use for AI assistants.
ml-dl-expert
by levy-n
Expert ML/DL teaching assistant for Hebrew University AI Engineering course. Activates for ANY machine learning or deep learning question: neural networks, PyTorch, TensorFlow, transformers, BERT, GPT, RAG, embeddings, CNNs, RNNs, LSTM, NLP, computer vision, clustering, regression, classification, training loops, backpropagation, loss functions, optimization, HuggingFace, vector stores, FAISS, ChromaDB, recommender systems, matrix factorization, transfer learning, data augmentation, autoencoders, Word2Vec, TF-IDF, topic modeling, prompt engineering, fine-tuning, LoRA, QLoRA, PEFT, quantization, sentiment analysis, image classification, object detection, time series, XGBoost, Random Forest, PCA, t-SNE, DBSCAN, K-Means, data pipeline, PDF parsing, chunking, function calling, AI agents, MLflow, W&B, experiment tracking, hyperparameter tuning, Optuna, SHAP, feature importance, Grad-CAM, model interpretability, reinforcement learning, Q-learning, DQN, PPO, policy gradient, GANs, VAE, diffusion models, Stable Diffusion, generative AI, model deployment, MLOps, synthetic data, data sourcing, Kaggle, dataset, data augmentation, SMOTE. Routes to 17 specialized sub-skills and provides code examples, visual diagrams, and Hebrew explanations when needed.