Skill UI

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Analyzes Excel and CSV uploads with DuckDB, inspecting schemas, running SQL, summarizing statistics, and exporting results so users can explore structured data efficiently.

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 safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

Helps implement optimized Power BI semantic models by analyzing the active model with MCP tools, then advising on star schemas, DAX, relationships, security, documentation, and performance best practices for reporting scenarios.

Orchestrate Segment customer-data actions through Rube MCP and Composio, covering event tracking, user identification, grouping, page views, batch dispatches, alias merges, and source management while always refreshing schema info first.

A specialized skill for generating comprehensive medical and scientific documentation, including case reports (CARE guidelines), diagnostic reports, clinical trial reports (CSR), and patient records (SOAP, H&P). It ensures strict adherence to global regulatory standards such as HIPAA, FDA, and ICH-GCP, and integrates mandatory AI-generated scientific schematics for enhanced clarity and publication quality. Ideal for medical research and healthcare compliance.

This skill guides systematic scientific inquiry by helping users develop testable, evidence-based hypotheses from observations and preliminary data. It strictly follows the scientific method, covering literature review, synthesizing evidence, generating multiple competing mechanisms, evaluating hypothesis quality (testability, falsifiability), and designing detailed experimental protocols. Comprehensive reports mandate the use of scientific schematics for visualization.

This skill manages the complexities of Clari API version changes, ensuring smooth transitions (e.g., v3 to v4). It guides developers through detecting schema drift, performing data field compatibility checks, and executing necessary database migrations (SQL/Python), ensuring reliable data pipelines after platform updates.

A comprehensive guide to designing high-performance schemas in ClickHouse. Learn how to select the optimal MergeTree engine (e.g., ReplacingMergeTree, AggregatingMergeTree), structure the ORDER BY key for maximum query efficiency, and implement effective partitioning strategies for large-scale analytical workloads and ETL processes.