ML Ops
Machine learning operations
ml-advanced
by levy-n
Implements ensemble learning (Random Forest, XGBoost, CatBoost, Stacking) and unsupervised methods (K-Means, DBSCAN, Hierarchical clustering, PCA, t-SNE, UMAP), and recommender systems (Matrix Factorization, NeuMF). Use when comparing gradient boosting algorithms, doing customer segmentation, anomaly detection, dimensionality reduction, building recommender systems, or when user mentions 'ensemble', 'boosting', 'bagging', 'random forest', 'XGBoost', 'clustering', 'K-Means', 'DBSCAN', 'elbow method', 'silhouette score', 'PCA', 't-SNE', 'dimensionality reduction', 'feature importance', 'matrix factorization', 'NeuMF', 'recommender system', or 'collaborative filtering'.
ml-teaching-assistant
by levy-n
Explains ML/DL concepts with analogies, visual diagrams, and progressive complexity. Covers backpropagation, gradient descent, attention mechanisms, neural networks, ML project methodology, and 50+ other concepts. Also provides the 5-step ML workflow, anti-patterns checklist, and model selection decision trees. Use when user says 'explain', 'I don\'t understand', 'how does X work', 'teach me', 'why does', 'what is the intuition', 'how should I approach', 'best practice', 'common mistakes', 'workflow', 'methodology', or asks conceptual 'why' questions about any ML topic. Provides intuitive explanations before math, ASCII visualizations, everyday analogies, and corrects common misconceptions.
video-agent
by founderjourney
AI content generation suite with 35+ models. Image generation, video creation, audio processing via FAL AI, Google Vertex AI, ElevenLabs. Pipeline orchestration and cost management.
x-algo-ml
by CloudAI-X
Explain the Phoenix ML model architecture for X recommendations. Use when users ask about embeddings, transformers, how predictions work, or ML model details.
mlops-experiment
by levy-n
MLOps and experiment tracking for reproducible ML workflows. Covers MLflow, Weights & Biases (W&B), TensorBoard, hyperparameter tuning (Optuna, Ray Tune), model registry, experiment versioning, and production deployment patterns. Use when user asks about 'MLflow', 'W&B', 'Weights and Biases', 'experiment tracking', 'hyperparameter tuning', 'Optuna', 'model registry', 'TensorBoard', 'reproducibility', 'model versioning', 'ML pipeline', 'model deployment', 'logging', 'wandb', or 'Ray Tune'.
Video Editing
by 6missedcalls
Output: always tell the user the output file path and duration
pytorch-mastery
by levy-n
Implements PyTorch training patterns, data loading, and GPU optimization. Covers tensor operations, DataLoader/Dataset classes, training loops, CUDA memory management, and debugging common errors. Use when writing PyTorch code, debugging tensor shape mismatches, fixing CUDA OOM errors, optimizing training speed, or when user mentions 'PyTorch', 'tensor', 'DataLoader', 'training loop', 'GPU memory', 'CUDA', '.to(device)', 'model.eval()', 'torch.no_grad()', 'shape mismatch', 'environment setup', 'nvidia-smi', or 'CUDA setup'.
notion-mastery
by founderjourney
Sistema completo de productividad y CRM en Notion con integracion n8n. Usar cuando el usuario necesite gestionar tareas, proyectos, metas, pipeline de ventas, CRM de clientes, prospeccion con Apollo.io, o automatizar workflows entre Notion y otras herramientas. Activa con palabras como Notion, tareas, proyectos, CRM, pipeline, leads, Apollo, prospeccion, follow-up, deals, clientes.
workout-program-designer
by 10x-Anit
Custom training plans by goal (strength, cardio, flexibility). Progressive overload programming, rest day optimization, home vs gym adaptations, deload weeks.
model-interpretability
by levy-n
Model interpretability, explainability, and debugging tools. Covers SHAP (TreeExplainer, DeepExplainer, KernelExplainer), feature importance analysis, LIME, attention visualization, Grad-CAM for CNNs, confusion matrix analysis, error analysis patterns, and model fairness auditing. Use when user asks about 'SHAP', 'feature importance', 'explainability', 'interpretability', 'why did the model predict', 'Grad-CAM', 'LIME', 'attention weights', 'confusion matrix', 'error analysis', 'model debugging', 'fairness', 'bias detection', or 'what did the model learn'.
sequence-models
by levy-n
Implements sequence models for time series and text. Covers RNN fundamentals, LSTM/GRU architectures, time series forecasting, text generation with language models, and sequence classification. Use when working with sequential data, predicting time series, text generation, or when user mentions 'RNN', 'LSTM', 'GRU', 'vanishing gradient', 'hidden state', 'time series', 'sequence-to-sequence', 'text generation', 'next word prediction', or 'recurrent neural network'.
fine-tuning-peft
by levy-n
Expert guide for LLM fine-tuning and parameter-efficient training methods. Covers LoRA, QLoRA, PEFT library, adapter tuning, instruction tuning, quantization (GPTQ, AWQ, GGUF, bitsandbytes), dataset preparation for fine-tuning, Hugging Face Trainer/TRL, RLHF/DPO/ORPO alignment, and model merging. Use when user asks about 'fine-tuning', 'LoRA', 'QLoRA', 'PEFT', 'adapter', 'quantization', 'bitsandbytes', '4-bit', '8-bit', 'instruction tuning', 'RLHF', 'DPO', 'model merging', 'Unsloth', 'Axolotl', 'training custom models', 'TRL', or 'SFT'.
generative-models
by levy-n
Generative AI models: GANs, VAEs, Diffusion Models, and image generation. Covers GAN architecture (Generator/Discriminator), DCGAN, Wasserstein GAN, Variational Autoencoders, latent space interpolation, Diffusion models (DDPM), Stable Diffusion, conditional generation, and text-to-image. Use when user asks about 'GAN', 'generative adversarial', 'VAE', 'variational autoencoder', 'diffusion model', 'image generation', 'Stable Diffusion', 'DCGAN', 'Wasserstein', 'WGAN', 'latent space', 'generate images', 'text-to-image', 'DDPM', 'denoising diffusion', 'style transfer', or 'deepfake'.
tsfm-forecast
by dtsong
"Use this skill when generating time-series forecasting pipelines using foundation models. Covers TimesFM, Chronos, MOIRAI, and Lag-Llama model selection, DuckDB-based preprocessing code, Python inference generation, backtesting harnesses, multi-model comparison, and client forecast deliverables. Common phrases: \"time-series forecast\", \"demand forecasting\", \"TimesFM\", \"Chronos\", \"predict future values\", \"zero-shot forecast\". Do NOT use for ML model training or fine-tuning (use python-data-engineering), real-time/streaming forecasts (use event-streaming), or pipeline scheduling (use data-pipelines)."
SUITE_NAME
by dtsong
TRIGGER_DESCRIPTION. Use when USER_CONTEXT. Routes to specialists for CAPABILITIES.
transformers-llm
by levy-n
Implements Transformer models and LLM workflows. Covers attention mechanism, BERT fine-tuning, HuggingFace Transformers library (Tokenizer, Trainer, Pipeline), and LLM ecosystem (GPT, Claude, Gemini, Ollama). Use when fine-tuning language models, using HuggingFace, calling LLM APIs, or when user mentions 'transformer', 'attention', 'BERT', 'HuggingFace', 'tokenizer', 'fine-tuning', 'LLM', 'GPT', 'Claude', 'Gemini', 'prompt engineering', 'zero-shot', or 'few-shot learning'.
gemini-image
by akrindev
Generate images using Google Gemini and Imagen models via scripts/. Use for AI image generation, text-to-image, creating visuals from prompts, generating multiple images, custom aspect ratios, and high-resolution output up to 4K. Triggers on "generate image", "create image", "imagen", "text to image", "AI art", "nano banana".
ai-engineer
by aibangjuxin
You are a highly skilled AI Engineer specializing in the practical application of machine learning models. You are an expert in Python and popular AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn. You excel at data preprocessing, model training, evaluation, and deployment.
python-notebooks-async
by ahgraber
"Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing."
interview
by BenjaminG
Interview user to clarify any topic - exploring codebase, investigating issues, planning features, understanding requirements, or drilling into plans. Socratic questioning to uncover details.
spoint
by AnEntrypoint
"Build multiplayer physics games with the Spawnpoint engine. Use when asked to: create a game, add physics objects, spawn entities, build a map/level, handle player interaction, add weapons, respawn, scoring, create moving platforms, manage world config, load 3D models, add HUD/UI, work with the EventBus, or develop any app inside an apps/ directory."
ai-sdk-core
by BjornMelin
Expert guidance for AI SDK Core: text generation, structured data, tool calling (tool/dynamicTool), MCP integration (createMCPClient, Experimental_StdioMCPTransport), embeddings/reranking, provider setup, middleware, telemetry, and error handling. Use when building with generateText/streamText, generateObject/streamObject, tools (needsApproval, strict, inputExamples, activeTools, toolChoice, experimental_context), embeddings (embed/embedMany/rerank), or MCP tools/resources/prompts/elicitation.
checkpoint
by OmniNode-ai
Pipeline checkpoint management for resume, replay, and phase validation
pipeline-audit
by OmniNode-ai
Systematically audit an end-to-end multi-repo pipeline for integration correctness by proving every join between services with file-level evidence, dispatching parallel agents per repo and per proof category, and compiling a severity-ordered gap register with actionable tickets