Skill UI

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.

This comprehensive guide provides a structured set of prompts to manage the entire lifecycle of upgrading a legacy .NET solution. It covers everything from initial project classification and dependency compatibility review to code modernization (e.g., async conversion), updating CI/CD pipelines, and defining robust testing and deployment strategies. Ideal for large-scale migration of older .NET applications to modern frameworks like .NET 8.

This skill analyzes an entire .NET solution (containing multiple projects) to map out a systematic migration path from Oracle to PostgreSQL. It scans for common Oracle indicators (NuGet packages, connection strings, code usage) and classifies every project into categories like MIGRATE, SKIP, or ALREADY_MIGRATED. The output is a structured, actionable Master Migration Plan, helping development teams manage large-scale database transitions efficiently.

A comprehensive tool for planning and auditing programmatic SEO pages generated at scale from structured data sources (CSV, JSON, APIs). It guides best practices for designing templates, defining scalable URL systems, establishing internal linking strategies (Hub/Spoke), and implementing robust quality gates. Use this when ensuring content uniqueness, preventing thin content penalties, and mitigating risks associated with large-scale, data-driven content generation.

This comprehensive toolkit automates critical Webflow operations, including managing CMS collections (create, update, delete), site publishing, page inspection, and asset uploads. By connecting via Rube MCP, it allows developers to programmatically interact with Webflow's backend, making it ideal for implementing automated content workflows, CI/CD pipelines, and large-scale content management systems.

Arboreto is a powerful Python library designed for inferring Gene Regulatory Networks (GRNs) from various gene expression datasets, including bulk and single-cell RNA-seq data. It leverages advanced machine learning algorithms like GRNBoost2 and GENIE3. The library supports scalable, distributed computation using Dask, making it ideal for analyzing large-scale, complex transcriptomics datasets to identify critical transcription factor-target gene relationships.

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.

Dask is a powerful Python library designed for scalable, parallel, and distributed computing. It enables workflows for datasets that exceed available RAM or require processing across multiple machines. Users can scale familiar pandas and NumPy operations (DataFrames, Arrays) and process unstructured data (Bags) efficiently, making it ideal for large-scale ETL, analytics, and distributed machine learning tasks.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.

A comprehensive skill for building, operating, and automating complex, reproducible genomics workloads on the DNAnexus platform. It provides deep integration with the `dx` CLI, `dxpy` Python SDK, and workflow languages like Nextflow and WDL. Use it for data transfer, job monitoring, project automation, and ensuring secure, efficient execution of large-scale biological computations.

Modal is a serverless cloud platform designed for executing Python-based AI/ML workloads. It provides on-demand access to powerful GPUs (e.g., H100, A100), scalable serverless functions, and custom containerization. Use cases include deploying ML models, running large-scale training/inference jobs, creating web APIs, and scheduling data pipelines, all defined entirely in code.