MLOps
Operacions d’aprenentatge automàtic
ml-advanced
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
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
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.
Repository Patterns
Kaakati
"Repository interface and implementation patterns with offline-first strategies"
excel-lbo-modeler
Jst-Well-Dan
Build leveraged buyout (LBO) models in Excel with debt schedules and IRR analysis. Use when structuring LBO transactions or analyzing PE returns. Trigger with phrases like 'excel lbo', 'build lbo model', 'calculate pe returns'.
megaeth-developer
Demerzels-lab
End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.
Error Handling Patterns
Kaakati
"Exception classes, failure classes, Either type, and error handling strategies"
rei
Demerzels-lab
Set up Rei Qwen3 Coder as a model provider. Use when configuring coder.reilabs.org, adding Rei to Clawdbot, or troubleshooting 403 errors from Rei endpoints.
ActiveRecord Query Patterns
Kaakati
"Complete guide to ActiveRecord query optimization, associations, scopes, and PostgreSQL-specific patterns. Use this skill when writing database queries, designing model associations, creating migrations, optimizing query performance, or debugging N+1 queries and grouping errors."
whisper-test
TrevorS
Transcribe WAV audio files using OpenAI Whisper for intelligibility testing. Triggers on: "transcribe audio", "whisper test", "test audio output", "is the audio intelligible", "check speech quality", "run whisper", "speech to text test", "check if audio sounds right"
qwen3-tts-profile
TrevorS
Profile and benchmark qwen3-tts-rs inference. Runs e2e_bench with chrome tracing, flamegraph, or Nsight Systems inside a CUDA Docker container. Triggers on: "profile", "benchmark", "run profiling", "chrome trace", "flamegraph", "nsys", "perf trace", "how fast", "what's slow", "performance", "bottleneck"
mlops-experiment
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'.
announce
phrazzld
Launch post generator for multiple platforms. Creates Twitter, HN, Reddit, and Indie Hackers announcements. Use for product launches, major updates, or milestone announcements.
config-sync-overview
CsHeng
Provide structured config-sync system and directory overview. Use when config-sync architecture guidance is required.
pytorch-mastery
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
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.
model-interpretability
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
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
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'.
megaeth-developer
Demerzels-lab
End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.
generative-models
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
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
dtsong
TRIGGER_DESCRIPTION. Use when USER_CONTEXT. Routes to specialists for CAPABILITIES.
excel-dcf-modeler
Jst-Well-Dan
Build discounted cash flow (DCF) valuation models in Excel. Use when creating DCF models, calculating enterprise value, or valuing companies. Trigger with phrases like 'excel dcf', 'build dcf model', 'calculate enterprise value'.