Skill UI

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series analysis. It supports a wide range of temporal machine learning tasks, including classification, regression, clustering, forecasting, anomaly detection, and similarity search. Use this skill when working with sequential patterns, time-indexed observations, or complex temporal data that requires specialized algorithms.

A comprehensive, scalable Python toolkit designed for end-to-end single-cell RNA-seq data analysis. It supports crucial steps including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), cell clustering, and differential gene expression analysis. The skill provides ready-to-run scripts for analyzing various formats (e.g., .h5ad, 10X, R-native) and generating publication-quality visualizations and cell type annotations.

A comprehensive resource detailing the use of scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for the entire ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, model evaluation, and building production-ready pipelines.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables the creation of scalable, structure-preserving embeddings (2D/3D) for visualization, clustering preprocessing, and advanced machine learning feature engineering. It supports various metrics (e.g., Euclidean, Cosine) and supervised learning workflows.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

A comprehensive guide to optimizing query performance in Snowflake. Learn advanced techniques such as implementing clustering keys, utilizing materialized views, leveraging result caching, and analyzing query profiles. This guide also covers best practices for warehouse tuning and optimizing common SQL anti-patterns to ensure efficient data querying and cost management.

This skill ingests public complaints from X/Twitter, automatically detecting, classifying, and clustering reported bugs. It cross-references symptoms with repository evidence (commits, deploys) and assigns ownership using defined routing rules. It is essential for product teams needing to monitor product health and gather early bug signals from social media channels.

SERP-overlap-based keyword clustering for SEO content architecture. Groups related keywords by real Google rankings, builds hub-and-spoke topic clusters, and generates internal linking plans and visual cluster maps.

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

A utility designed to generate high-dimensional vector embeddings (384-dim) from various sources including text, code, or documents. This process converts raw data into numerical representations optimized for semantic search, similarity comparison, and clustering, utilizing ONNX and HNSW indexing for efficient retrieval.