Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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Select by study design before retrieval. The resource tool discloses package metadata and
instructions only after selection. Package-relative scripts, references, and assets support the
Agent's task-specific execution; they are not fixed query functions.
Define organism, biological system, condition, assay, study design, sample unit, required format, processing level, and reuse constraints. This step is complete when every known constraint and every unresolved requirement is explicit.
Browse the closest modality and compare the complete leaf metadata:
resource({ action: "list", category: "experimental-data/transcriptomics" })
This step is complete when kind is resource_metadata and every returned candidate has been
compared against the request.
Load each selected package with its exact exposed name:
resource({ action: "read", name: "biomarker-geo" })
This step is complete when each selected package's query interface, schema, access mode, and package-relative scripts, references, assets, and file conventions are known.
Accept a dataset only after verifying organism, assay, sample count, groups, processing state, access conditions, file sizes, checksums when available, and repository version. This step is complete when every candidate has an explicit inclusion or exclusion reason and every selected file is accounted for.
Write manifests, raw responses, downloaded files, and normalized metadata to the Session workspace. Report accession, retrieval date, selected files, sizes, checksums, and failures. This step is complete when every selected package has a result, a valid empty result, or a concrete failure and every artifact path is reported.
Use package directories as read-only sources; all generated output belongs in the Session workspace.
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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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