Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.

This skill provides comprehensive monitoring and comparative analysis of cross-chain bridges in the DeFi ecosystem. It tracks key metrics such as Total Value Locked (TVL), transaction volume, typical fees, and real-time transfer status across major networks (e.g., Ethereum, Arbitrum, Polygon). Use this tool to research bridge safety, compare transfer routes before depositing funds, or track specific transaction hashes across multiple protocols like Wormhole, LayerZero, and Stargate.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

DeepChem is a comprehensive Python library designed to apply machine learning to chemistry, materials science, and biology. It enables sophisticated molecular property prediction (ADMET, solubility, toxicity), drug discovery, and materials design. The library supports diverse featurization methods (fingerprints, graph representations) and advanced model architectures, including GNNs and transfer learning, making it ideal for analyzing complex chemical datasets like MoleculeNet.

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.

A comprehensive guide detailing the process of migrating data warehouses to Snowflake from major sources like Amazon Redshift, Google BigQuery, or on-premise databases. This resource covers critical steps including schema conversion (handling dialect differences like STRUCT/ARRAY to VARIANT), robust data transfer methods (S3/Cloud Storage, Python/Pandas), data validation techniques (checksums, row counts), and essential SQL syntax translation patterns.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This powerful tool compares identical tables across two SQL Server instances (e.g., Production vs. Staging). It efficiently detects missing rows, extra columns, schema drifts, and data mismatches. Leveraging Python and Apache Arrow for fast columnar data transfer, it provides a comprehensive reconciliation report, making it essential for data migration validation and ETL verification.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This guide provides an in-depth understanding of the peer-review process for Conservation Biology, covering double-blind review standards, editorial screening criteria, and expert review priorities. It teaches authors how to structure their research to explicitly demonstrate conservation relevance, novelty, and transferable implications, effectively stress-testing a manuscript against common desk-rejection grounds.

This guide provides a framework for rigorously defending the methodological design of scientific manuscripts in conservation biology. It covers advanced topics including detection modeling (occupancy, capture-recapture), addressing spatial structure and pseudoreplication, quasi-experimental designs (BACI, difference-in-differences), and systematic review protocols (PRISMA). The goal is to ensure that the evidence presented directly and credibly supports transferable, decision-relevant conservation conclusions.