Skill UI

Query gnomAD via GraphQL to retrieve allele frequencies, constraint scores, and loss-of-function annotations for population-informed variant interpretation and rare disease gene prioritization.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

Batch-categorize uncategorized transactions by clustering similar descriptions, researching unknown merchants, and creating rules or annotations so future payments auto-classify with minimal manual review.

Provides direct REST API access to UniProt, the premier resource for protein sequence and functional information. This skill enables users to search proteins by name, gene, or accession number, retrieve sequences in FASTA format, perform cross-database ID mapping (e.g., Ensembl, RefSeq), and analyze functional annotations (GO terms) for building comprehensive bioinformatics pipelines.

This comprehensive guide outlines idiomatic and high-quality coding standards for Dart and Flutter. It covers best practices for naming conventions, type annotations, class structure, file organization, and documentation, ensuring developers write clean, readable, and maintainable Dart codebases.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

Provides an expert Spring Boot 4 testing guide that explains the test pyramid, slice annotations, AssertJ-centric assertions, MockMvcTester, RestTestClient, Testcontainers, and practical coverage goals.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

This skill provides a comprehensive framework for securely inspecting and automating complex microscopy data workflows against OMERO.server. It utilizes omero-py and BlitzGateway to manage metadata, annotations, ROIs, tables, and raw files. Ideal for conducting scoped inventory, structured data export/import planning, and performing reviewed write operations while adhering to strict data security protocols.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.