Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features".
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".
Split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning".
Setup machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking...
Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phr...
Investigate noisy/common alerts and create false positive (FP) rules to suppress benign detections. Analyzes detection frequency over 7 days, identifies patterns, generates and tests FP rules with operator appr...
深度学习论文复现的skill。可以读取pdf并解析其中的图片、公式、表格等内容，然后参考下面的prompts。触发词包括"帮我复现这篇论文"、"论文复现"、"实现这个模型"，或当用户提供深度学习论文需要转化为PyTorch代码时。
Expert in vector spaces, matrices, linear transformations, eigenvalues, and applications to data science and machine learning
Discover and implement EachLabs AI providers. Use when: checking for new EachLabs models, implementing EachLabs providers, updating providers.md status, or working with EachLabs API integrations.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt temp...
Hugging Face Hub integration for model and dataset operations
Generate production-ready forecasting experiments with StatsForecast and TimeGPT. Use when setting up model benchmarking or cross-validation. Trigger with 'scaffold experiment' or 'compare models'.
Audits Nixtla library usage and recommends cost-effective routing strategies. Scans TimeGPT, StatsForecast, and MLForecast patterns, identifies cost optimization opportunities, generates comprehensive usage rep...
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series...
Analyze and transform code for TimeGPT-1 to TimeGPT-2 migration. Use when upgrading TimeGPT version. Trigger with 'migrate to TimeGPT-2' or 'upgrade TimeGPT'.
Transform forecasting experiments into Airflow/Prefect pipelines with monitoring. Use when deploying forecasts to production. Trigger with 'generate pipeline' or 'create Airflow DAG'.
Forecast multiple time series in parallel using TimeGPT. Use when processing 10-100+ contracts efficiently. Trigger with 'batch forecast' or 'parallel forecasting'.
Transform prediction market data to Nixtla format (unique_id, ds, y). Use when preparing datasets for forecasting. Trigger with 'convert to Nixtla format' or 'schema mapping'.
Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Acti...
Forecasts multiple time series in parallel batches using TimeGPT API. Optimizes throughput with rate limiting and supports portfolio aggregation. Use when processing 10-100+ contracts or needing efficient multi...
Generate production-ready TimeGPT forecasting pipeline code from requirements. Use when scaffolding a pipeline with validation, logging, visualization, and repeatable runs. Trigger with "create TimeGPT pipeline...
Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmar...
Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with "auto-select model", "choose...
Scaffolds production-ready forecasting experiments with Nixtla libraries. Creates configuration files, experiment harnesses, multi-model comparisons, and cross-validation workflows for StatsForecast, MLForecast...
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.