Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Expert MLOps engineer specializing in ML infrastructure, platform engineering, and operational excellence for machine learning systems. Masters CI/CD for ML, model versioning, and scalable ML platforms with foc...
Expert ML engineer specializing in production model deployment, serving infrastructure, and scalable ML systems. Masters model optimization, real-time inference, and edge deployment with focus on reliability an...
Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reli...
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...
Expert trend analyst specializing in identifying emerging patterns, forecasting future developments, and strategic foresight. Masters trend detection, impact analysis, and scenario planning with focus on helpin...
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, e...
Data processing, analysis, and visualization with Python/JavaScript. Use for data exploration, pandas operations, chart generation, and insights extraction.
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations...
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 valid...
Complete MLOps skill covering production ML lifecycle and security. Includes data ingestion, model deployment, drift detection, monitoring, plus ML security (prompt injection, jailbreak defense, RAG security, p...
Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
Walk-forward analysis for trading strategies. Tests whether optimized parameters hold up on out-of-sample data. Use when checking parameter robustness, detecting potential overfitting, or validating a backtest.
Use when you need to empirically test whether hypothesized symmetries actually hold in your data or model. Invoke when user mentions testing invariance, validating equivariance, checking if symmetry assumptions...
Use when you have validated symmetry groups and need to design neural network architecture that respects those symmetries. Invoke when user mentions equivariant layers, G-CNN, e3nn, steerable networks, building...
Use when you have implemented an equivariant model and need to verify it correctly respects the intended symmetries. Invoke when user mentions testing model equivariance, debugging symmetry bugs, verifying impl...
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when "prompt engineering, system prompt, few-shot,...
Patterns for generating synthetic data for ML training, testing, and privacy. Covers LLM-based generation, tabular synthesis, and quality validation. Use when "synthetic data, generate training data, fake data...
Use when adapting large language models to specific tasks, domains, or behaviors - covers LoRA, QLoRA, PEFT, instruction tuning, and full fine-tuning strategiesUse when ", " mentioned.
Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edg...
Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences....
Patterns for multi-sensor data fusion including Kalman filters (KF, EKF, UKF), complementary filters, particle filters, and factor graph optimization. Covers state estimation, covariance tuning, outlier rejecti...
Use when automating model architecture design, optimizing hyperparameters, or exploring neural network configurations - covers NAS algorithms, search spaces, Bayesian optimization, and AutoML toolsUse when ", "...
Use when training models across multiple GPUs or nodes, handling large models that don't fit in memory, or optimizing training throughput - covers DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient...
Use when reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.