Skill UI

Guides creating, altering, and validating Snowflake semantic views via the Snowflake CLI, covering connection setup, star-schema modeling, metadata comments/synonyms, temporary validation steps, and post-validation querying for reliable semantic layer delivery.

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.

Automates WhatsApp Business operations via Composio's Rube MCP toolkit, covering text, template, media, reply, profile, and contact workflows after first querying the latest tool schemas.

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.

This technique optimizes search precision when querying blog posts or articles. By wrapping unique phrases, specific titles, or direct quotes in quotation marks, the system treats the phrase as a single, exact unit. This prevents the search engine from breaking the phrase into individual words, which dramatically reduces noise and increases the likelihood of finding the intended source material.

This skill provides comprehensive integration with the Benchling cloud platform using the Python SDK and REST API. It allows users to programmatically manage critical life sciences data, including registry entities (DNA/RNA/proteins), inventory tracking, ELN entries, workflows, and querying the Data Warehouse. Ideal for automating complex lab data pipelines and syncing Benchling with external systems.

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 programmatic access to massive, versioned public single-cell and spatial transcriptomics datasets from CZ CELLxGENE. This skill allows users to efficiently query specific subsets of cell metadata, gene expression matrices, embeddings, and summary statistics across various organisms, tissues, and diseases. It is ideal for large-scale, memory-efficient biological research and model training without full dataset downloads.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

LaminDB is an open-source, lineage-native lakehouse specifically designed for complex biological datasets and models. It provides unified capabilities for querying, tracking data lineage, validating schemas, and ensuring FAIR compliance across diverse data types (e.g., AnnData, Parquet, Zarr). Use this skill when managing multi-modal genomic data, tracking computational workflows (like Nextflow/Snakemake), or building robust, reproducible data infrastructure in bioinformatics.

This skill provides a comprehensive workflow for querying documented, authoritative public APIs across scientific, environmental, and medical domains (e.g., NASA, PubChem, USGS). It ensures that data retrieval is reproducible, auditable, and traceable by defining explicit endpoints, handling pagination, and verifying data provenance, rather than relying on general knowledge inference.

This comprehensive workflow manages the full property lifecycle using the AppFolio API. It handles authentication, creating and updating property records and units, onboarding new tenants, generating lease agreements, and querying real-time occupancy/vacancy data. Ideal for automating core real estate management tasks in a SaaS environment.