Skill UI

A comprehensive guide to MATLAB and GNU Octave, covering core scientific computing tasks. This skill details capabilities in matrix operations, linear algebra, data visualization (2D/3D), statistical analysis, signal processing, and solving differential equations. It is ideal for users requiring complex numerical simulation, data modeling, and scientific scripting.

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

A comprehensive Python wrapper for OpenMS, designed for sophisticated computational mass spectrometry workflows. It supports end-to-end analysis in proteomics and metabolomics, enabling feature detection, peptide/protein identification, quantitative analysis (label-free/isobaric), and adduct/accurate mass annotation. Ideal for researchers processing complex LC-MS/MS raw data and running high-level pipelines.

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.

scVelo is a powerful Python package designed for RNA velocity analysis using single-cell RNA-seq data. It estimates cell state transitions by modeling the kinetics of mRNA splicing—specifically using the ratio of unspliced to spliced mRNA abundances. This allows researchers to reconstruct developmental trajectories and predict cell fates, even when only snapshot data is available, making it a vital complement to standard trajectory inference tools like Scanpy.

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.

Vaex is a high-performance Python library designed for processing and visualizing enormous tabular datasets that exceed available RAM (gigabytes to terabytes). It excels at out-of-core DataFrame operations, lazy evaluation, fast statistical aggregation, and machine learning integration on massive datasets. Use it for analyzing large CSV, Parquet, or HDF5 files efficiently on a single machine, without memory constraints.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.

Builds a robust, automated workflow to export critical sales data—including forecasts, quota details, adjustments, and CRM totals—from Clari CRM to a designated data warehouse. The pipeline supports multiple targets like Snowflake, BigQuery, and PostgreSQL, ensuring that real-time revenue intelligence is available for advanced analytics and business reporting.

This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.

This reference architecture provides a comprehensive blueprint for building a robust revenue intelligence data platform integrated with Clari. It covers the entire data lifecycle, including API data extraction (ETL pipeline), data warehousing design (Snowflake/BQ), change detection logic, and advanced analytics/alerting layers. Use this guide when designing a scalable forecast tracking system, planning complex data integrations, or establishing best practices for enterprise revenue analytics.