K-Dense

Search biomedical papers, regulatory documents, clinical trials, and protein records through GXL Paperclip. Extract comparable information across documents, inspect figures, and organize findings with citations linked to the source lines that support them.

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• Finding biomedical papers on a topic
• Comparing methods and results across papers
• Reviewing regulatory documents and clinical trials

Process calibrated one-dimensional NMR signals into spectra, candidate peaks, and signed integration results. Supports manual phase adjustment, baseline correction, and recorded processing settings so results can be reviewed and reproduced.

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• Convert raw one-dimensional NMR signals into spectra
• Check peak positions against reference signals
• Review phase and baseline corrections

Helps build LabArchives connections for electronic lab notebooks and laboratory inventory. Guides endpoint selection, request signing for authentication, and user authorization, with local tools to inspect container files and prepare redacted request plans before remote operations.

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• Plan an electronic lab notebook API connection
• Check authentication for laboratory inventory requests
• Inspect an LA container file locally

Finds papers, preprints, citation links, and openly available full text through 18 scholarly APIs. Selects databases for the task and returns relevant records with search parameters, identifiers, and access dates so the retrieval can be repeated.

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• Find papers and preprints on a research topic
• Look up a paper by DOI, PMID, or arXiv ID
• Find openly available full text or PDFs

Prepare and launch nf-core/pacsomatic workflows for matched tumor and normal PacBio HiFi samples. Generate sample sheets and launch files from unaligned BAM inputs, run local checks, submit cluster launchers, and troubleshoot startup failures.

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• Prepare sample sheets for matched tumor and normal samples
• Review generated launch files with a dry run
• Submit a Nextflow launcher to a computing cluster

Organize research sources in self-hosted Open Notebook, then search them, ask questions with citations, create notes, transform text, or generate podcasts with multiple speakers. Supports REST API automation and configuration of local or cloud AI providers.

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• Collect PDFs and web sources in a research notebook
• Ask questions about selected sources with citations
• Apply custom summaries and text transformations

Process satellite imagery and map data for geospatial research. Helps align coordinate systems and image grids, calculate vegetation and terrain measures, summarize data by area, and analyze road networks while preserving masks and source information.

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• Align coordinate systems and raster grids
• Calculate vegetation indices from satellite imagery
• Analyze slope and aspect from elevation data

Organize MRI, EEG, and other biomedical research data using BIDS, a shared standard for filenames, folders, and metadata. Query datasets, check compliance, convert DICOM files, and prepare metadata or processed outputs for analysis and sharing.

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• Organize MRI and EEG files using BIDS conventions
• Find files by participant, session, or task
• Convert DICOM data and prepare metadata

Use Biohub’s Python SDK to complete protein sequences with ESM3, extract numerical sequence representations with ESMC, and predict all-atom structures with ESMFold2. The skill guides local or hosted inference, input validation, and output checks.

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• Completing masked positions in protein sequences
• Extracting numerical features from protein sequences
• Predicting all-atom protein structures

Inspect supported local scientific files before modeling, summarize structure and missing values, and flag possible data leakage or outlier effects. Produces bounded aggregate reports with identifiers obscured and provides a structured analysis report template.

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• Check local CSV or TSV data before modeling
• Audit missing values and overlap between data splits
• Compare outlier influence and transformation sensitivity

Build and simulate quantum circuits in Python with Cirq. Compare noise effects, test parameter values, transform circuits for hardware constraints, and prepare experiments for Google Quantum AI or supported providers when the required access is available.

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• Building and simulating quantum circuits
• Comparing measurement results at different noise levels
• Testing circuits across a range of parameter values

Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.

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• Transfer and organize genomics data on DNAnexus
• Validate app configuration and build applets
• Import WDL, CWL, or Nextflow workflows

Retrieve and combine protein annotations, biological pathways, and chemical identifiers from services such as UniProt, KEGG, and UniChem using Python. Supports cross-database lookups and batch identifier conversion while preserving multiple matches and failed requests.

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• Collect protein sequences and functional annotations
• Retrieve pathway information for genes
• Match compound identifiers across databases

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

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• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Prepare genomic interval data for machine learning by checking BED files, reference genome consistency, and model compatibility. Plan Region2Vec or scEmbed workflows and assess shared reference regions using local validation and planning helpers.

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• Check BED coordinates and reference genome consistency
• Plan Region2Vec or scEmbed training
• Inspect model and tokenizer compatibility

Simulate a uniform gas mixture in a closed, insulated reactor at constant volume or pressure. Calculate ignition delay from the peak temperature rise rate, export temperature and species histories, and check conservation and numerical stability.

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• Calculate ignition delay at constant volume or pressure
• Inspect temperature and species histories
• Compare ignition delays across reaction mechanisms

Implement and debug astronomical data analysis with Astropy in Python. Work with physical units, celestial coordinates, observation files, tables, precise time conversions, and cosmological models to prepare data and calculate astronomical quantities.

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• Convert celestial coordinates and physical units
• Read and edit FITS observation files
• Match objects across astronomical catalogs

Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

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• Planning analytical validation studies and acceptance criteria
• Evaluating calibration, accuracy, and precision
• Estimating and confirming detection and quantitation limits

Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.

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• Compare repeated experiments to improve model training
• Improve an agent system's measured success rate
• Test prompt changes against a fixed evaluator

Analyze 2D fluorescence microscopy images to detect nuclei and measure their counts, intensity, and area. Run repeatable CellProfiler batches, inspect nucleus outlines, and review measurement tables with recorded pipeline settings and quality checks.

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• Count nuclei across microscopy image batches
• Measure fluorescence intensity and area for each nucleus
• Inspect nucleus outlines for segmentation errors

Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.

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• Rank candidate regulator–target gene relationships
• Compare GRNBoost2 and GENIE3 results
• Check network stability across seeds and resampled data

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

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• Plan binding screens for candidate protein sequences
• Estimate experiment costs before submission
• Track laboratory progress and retrieve results

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

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• Classify time series and predict continuous outcomes
• Group time series with similar patterns
• Forecast future values