Skill UI

NeMo Curator streams GPU-accelerated curation for LLM training, with super-fast fuzzy deduplication, multimodal quality filters, semantic dedupl, PII redaction, NSFW checks, and RAPIDS-scaled pipelines for prepping massive corpora.

Query UCSF’s ZINC22 repository of 230M+ purchasable compounds via the web UI or CartBlanche APIs to fetch IDs/SMILES, run similarity, supplier, or random sampling, and assemble docking-ready libraries for virtual screening.

The BIDS skill provides a comprehensive framework for organizing, standardizing, and managing complex neuroscience and biomedical datasets. It covers querying, validating, and converting various modalities (MRI, EEG, MEG, etc.) to ensure compliance with the BIDS standard for research reproducibility and efficient data sharing.

This skill provides an end-to-end, reproducible workflow for analyzing matched tumor-normal pairs using the nf-core/pacsomatic pipeline. It handles input validation (BAM files, IDs), generates necessary samplesheets and launch artifacts, and supports execution across various environments, including local runs and cluster schedulers (Slurm, LSF). It is ideal for initiating, validating, or troubleshooting the entire analysis process.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

This skill provides comprehensive, read-only market intelligence for prediction markets. It helps users research market events, compare different venues (like Polymarket, Kalshi), analyze liquidity, and understand underlying assets. It generates source-grounded briefings, emphasizing context and data quality, and strictly avoids providing investment or trading advice.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A comprehensive guide for creating self-contained, high-quality tables and figures for demography manuscripts. It outlines best practices for generating distinctive demographic graphics—such as population pyramids, Lexis surfaces, and survival curves—ensuring they are accessible, reproducible, and adhere to journal limits.

A comprehensive guide for researchers ensuring the highest level of open-science reproducibility for academic submissions (e.g., FSE). It details how to map every research claim (numbers, techniques) to verifiable evidence, pin technical provenance (SHAs, model IDs), manage data availability statements, and achieve defined degrees of reproducibility (Turnkey, Scripted).

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.

This comprehensive guide assists researchers in determining the suitability of their manuscript for Nucleic Acids Research (NAR). It details the journal's scope, structural demands, and rigorous standards for submissions involving DNA, RNA, bioinformatics, databases, or web tools. Use this to assess the fit and required elements before official submission.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.