Literature: retrieve document-level scientific evidence from publications, clinical-trial registries, and patent sources. Use for PubMed records, reproducible searches, trial registrations, patent evidence, abstracts, study metadata, stable identifiers, and citation provenance.
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Build traceable evidence from publications, clinical trials, and patents while preserving each
document type's identifiers, search fields, status semantics, and evidentiary role. The resource
tool exposes source metadata and instructions; the Agent constructs the task-specific search.
Define the question, document type, date range, organism or population, intervention or exposure, study design, identifiers, language constraints, and required metadata. This step is complete when the search protocol and unresolved constraints are explicit.
Browse the relevant document source:
resource({ action: "list", category: "literature/publications" })
This step is complete when kind is resource_metadata and every returned source has been
compared against the protocol.
Load the selected source instructions using its exact exposed name:
resource({ action: "read", name: "biomarker-pubmed" })
This step is complete when the exact query interface, fields, pagination, identifiers, and output schema and package-relative scripts, references, and assets are known.
Execute reproducible fielded queries through all result pages. Preserve raw responses, query syntax, retrieval date, and stable identifiers such as PMID, NCT, and patent publication numbers. This step is complete when every page has been retrieved or a concrete failure is recorded.
Deduplicate by stable identifiers and label every record by document type and evidentiary role. Emit citations exclusively from retrieved metadata. This step is complete when every retained record has traceable provenance and every selected source has a result, valid empty result, or concrete failure.
Write search logs, raw metadata, normalized tables, and citation exports to the Session workspace; use package directories as read-only sources.
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Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
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ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.
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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
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