Skill UI

Generates a complete, compilable xUnit integration test project structure for validating complex database migrations, specifically from Oracle to PostgreSQL, within .NET solutions. This scaffolding includes a transaction-rollback base class and a seed data manager, ensuring a robust environment for writing migration integration tests without requiring test cases. Ideal for setting up testing infrastructure before migration validation.

A universal assistant for conducting deep code reviews across major SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). It rigorously analyzes SQL code for critical vulnerabilities (like SQL injection), performance bottlenecks, maintainability issues, and adherence to industry best practices. Features include security audits, index optimization recommendations, query structure analysis, and schema design validation, ensuring robust and secure database development.

Cross-database SQL performance optimization assistant offering query tuning, indexing strategies, pagination, batch operations, and monitoring guidance for MySQL, PostgreSQL, SQL Server, Oracle, and other relational systems.

Automates Supabase projects by sequencing Composio Rube MCP workflows for database querying, schema inspection, SQL execution, project/org management, storage buckets, edge functions, and health monitoring while always refreshing tool schemas first.

This technique allows users to locate game developers or projects indirectly by searching for highly distinctive biographical details. Instead of relying on game databases, it leverages unique phrases, historical events, or personal lifestyle details found in interviews, blogs, or conference talks. It is invaluable when the developer's identity is obscured, requiring deep research using exact-match queries on obscure information.

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.

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

This technique leverages structured data from music awards (like Grammy nominations and wins) to act as powerful search anchors. By extracting clues such as the award category, year, and status (nominated/won), users can cross-reference potential musicians against verifiable award databases. This method significantly narrows down the pool of candidates, allowing for highly accurate identification and verification of artists.

A powerful tool for scientific literature review and evidence synthesis. This skill searches a curated database of full-text scientific papers, going beyond traditional abstracts. It extracts and returns highly structured, quantitative data, including methodologies, quantitative results, sample sizes, quality scores, and 25+ metadata fields per paper, making it ideal for meta-analyses and systematic reviews.

Biopython is a robust, comprehensive toolkit for computational molecular biology written in Python. It enables users to manipulate biological sequences (DNA, RNA, protein), parse standard formats (FASTA, GenBank, PDB, etc.), and programmatically access major bioinformatics databases like NCBI via Entrez. This skill is ideal for developing complex bioinformatics pipelines, including sequence alignment, structural analysis, and phylogenetic tree construction.

A comprehensive Python package providing a unified interface to over 40 major bioinformatics web services and databases. Use it for complex biological workflows, including cross-database querying (e.g., UniProt, KEGG, ChEMBL), identifier mapping, protein sequence retrieval, pathway analysis, and running sequence similarity searches like BLAST. It streamlines data integration from multiple life science resources into consistent Python code.

Provides comprehensive tools for academic research, allowing users to search databases like Google Scholar and PubMed. It extracts accurate metadata, validates citations, and generates standard formats like BibTeX. The skill ensures reference accuracy and supports the creation of scientific schematics to enhance document visualization.