Skill UI

LatchBio Integration provides a Python-first platform to compose, deploy, and monitor serverless bioinformatics pipelines with the Latch SDK, decorator-based tasks, cloud storage (LatchFile/Dir), GPU tuning, and Nextflow or Snakemake interoperability.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

This internal agent skill automates the complex process of determining the most likely owner or team for a bug cluster. It follows a strict 6-level precedence model, starting with cached overrides, and sequentially querying sources like Service Owners, Oncall status, CODEOWNERS, recent assignees, and committers. The process includes staleness detection and confidence scoring, providing highly accurate and auditable routing recommendations for internal development triage.

This technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

A powerful tool for executing complex, multi-step, and multi-agent workflows defined in YAML format directly within the AI session. It acts as an orchestration engine, simulating various roles (agents) to complete tasks sequentially or in parallel, without requiring external API keys or configuration. Ideal for complex business process automation and multi-role content generation.