Skill UI

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

This skill provides read-only functionality to query and list comprehensive information about KubeSphere clusters. It is designed for viewing cluster lists, checking current status, retrieving detailed specifications, and identifying component versions across multiple clusters. IMPORTANT: This operation is strictly read-only. Never use these commands for creating, updating, or deleting resources; always use the KubeSphere console for management actions.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

A technique for dramatically increasing the hit rate when searching for TV series. Instead of using generic terms like genre or era, combine 2-3 highly distinctive plot details (e.g., character sacrifices, specific season incidents) into a single query. This method leverages the unique nature of plot points to pinpoint specific shows quickly.

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

This skill teaches the methodology of forming concrete hypotheses about specific entities (such as film titles, director names, or historical events) when presented with constraints. Instead of performing broad, generic searches, users should leverage their world knowledge to narrow down possibilities, turning a wide query into a precise, verifiable search query. This is particularly effective for academic or historical research.

WizTelemetry Logging is a core extension component within the KubeSphere Observability Platform, designed for comprehensive log collection, processing, and storage. It supports advanced log collection architectures, including collecting container stdout/stderr logs (via vector-logging) and collecting logs directly from the container filesystem (via logsidecar-injector). Use this guide to understand its configuration, dependencies, and how to query logs using the dedicated API.

A comprehensive integration tool for automating lab data management within the Benchling cloud platform. It provides programmatic access via the Python SDK and REST API to core life science entities, including registry management (DNA, RNA, proteins), inventory tracking, Electronic Lab Notebook (ELN) entries, workflows, and data warehouse querying. Ideal for syncing data, building complex automations, and executing large-scale R&D data pipelines.

Provides programmatic and standardized access to massive, versioned datasets of single-cell and spatial transcriptomics from CZ CELLxGENE. This skill allows users to query millions of cells, analyze gene expression, and access pre-calculated embeddings across various tissues, diseases, and species without downloading entire datasets. Essential for large-scale bioinformatics analysis, machine learning model training, and comprehensive cross-dataset comparison.

This tool provides access to the Cancer Dependency Map (DepMap) data, derived from genome-wide CRISPR screens, RNAi, and compound sensitivity assays. It allows users to query gene dependency scores, identify cancer-specific vulnerabilities, predict synthetic lethal interactions, and validate potential oncology drug targets across various cancer cell lines.

The ETE Toolkit is a powerful Python library designed for analyzing, manipulating, comparing, and visualizing phylogenetic trees stored in Newick or Nexus formats. It allows users to perform topology edits, calculate phylogenetic distances (e.g., Robinson-Foulds), detect evolutionary events, and query external taxonomic databases like NCBI/GTDB. Note that this tool analyzes existing trees; it is not used for inferring trees from raw sequences.

gget is a comprehensive command-line and Python package providing unified access to over 20 major bioinformatics databases. Users can perform deep analyses across genomics, proteomics, and disease associations. Key functions include gene information lookups, sequence alignment (BLAST), structural prediction (AlphaFold), single-cell analysis, and enrichment studies, supporting both interactive queries and automated pipelines.