K-Dense

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Simulate quantum dynamics, steady states, and spectra with QuTiP 5. The skill guides solver selection and checks physical assumptions, units, state validity, and numerical convergence so local simulation results can be reviewed with their limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Simulating quantum dynamics with environmental dissipation
• Choosing a solver for a quantum model
• Checking numerical convergence and state validity

Read, query, filter, and write genomic files with pysam in Python. Extract regions, summarize variants, and count aligned bases while handling coordinate conventions, file indexes, headers, and the reference sequences needed to decode CRAM files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect genomic file formats, headers, and indexes
• Filter alignment records by quality and flags
• Summarize variants and genotypes in a genomic region

Prepare and run molecular dynamics simulations with OpenMM, then analyze molecular motion with MDAnalysis. Examine protein flexibility, structural changes, and protein–ligand contacts through calculations and plots of simulated trajectories.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating protein stability after a mutation
• Measuring structural changes and flexibility
• Tracking protein–ligand contacts

Create Markdown research notes, reports, and guides with Mermaid diagrams, which describe relationships as editable text. Choose suitable diagrams for workflows, timelines, or schemas, add accessible descriptions, and check rendering in the intended destination.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing research notes with workflow diagrams
• Diagramming data structures and relationships
• Creating project timelines

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screen compound libraries with drug-likeness rules
• Flag PAINS and other structures for review
• Combine property, structure, and complexity filters

Build, run, and troubleshoot data processing pipelines with Nextflow and nf-core. The skill guides modular workflow development, input preparation, testing, cached restarts, and configuration for local computers, computing clusters, or cloud environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run an nf-core workflow with a samplesheet
• Build and test reusable DSL2 modules
• Investigate failed runs and restart with cached results

Measure fluid velocity from pairs of particle images with OpenPIV. The skill covers image preparation, velocity calculation, correction of unreliable vectors, plotting, and calculation of vorticity and strain rate to examine flow patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measure velocity fields from experimental image pairs
• Detect and replace unreliable velocity vectors
• Calculate vorticity and strain rate

Prepare small image tiles from whole-slide histology scans with Histolab. Inspect tissue regions, choose random, grid, or score-based extraction, standardize H&E staining, and organize tile datasets with patient-level splits and visual checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting training tiles from whole-slide histology scans
• Previewing tissue masks and tile locations
• Standardizing H&E stain appearance

Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect physiological recordings before preprocessing
• Analyze ECG and heart rate variability for research
• Plan event windows and baseline correction

Turn observations or preliminary findings into testable research plans. The skill separates candidate hypotheses from evidence, develops competing explanations and distinguishing predictions, and helps specify measurements and analysis plans for preregistration, a research plan recorded before testing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn an observation into testable research hypotheses
• Compare competing explanations and distinguishing predictions
• Define measurements and review an analysis plan

Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register and organize biological datasets
• Validate DataFrame or AnnData metadata
• Standardize annotations with biological vocabularies

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Create scientific charts in Python with detailed control over axes, colors, labels, legends, and layout. Arrange multiple plots in one figure and export PNG, PDF, or SVG files for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create charts from experimental data
• Arrange multiple plots in one figure
• Adjust colors, labels, and annotations

Convert single-plane, single-channel two-photon TIFF images and timestamped behavioral position CSV files into NWB, a neuroscience data format. Preserve metadata, check clock alignment against acquisition evidence, and report format validation findings and source-to-output comparisons.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting two-photon TIFF images to NWB
• Combining imaging and behavioral position records
• Checking clock alignment using synchronization pulses

Match scientific labels to ontology identifiers, standardized IDs for concepts such as tissues, cell types, and diseases. Checks existing IDs, labels, obsolete terms, and prefixes to support metadata annotation, submission preparation, and table audits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Annotating tissue, cell type, or disease fields with standard IDs
• Auditing metadata tables for incorrect IDs and labels
• Checking obsolete terms and available replacements

Inspect microscopy data stored on OMERO.server, export selected image metadata, and plan imports or exports. The skill uses explicit data scopes and limits, with dry-run helpers and reviewed targets for workflows that change server data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inventory selected OMERO objects with result limits
• Export annotations and regions of interest as JSON
• Plan microscopy image imports and exports

Create, review, migrate, and simulate Python liquid-handling protocols for Opentrons Flex and OT-2 robots. Covers deck layout, labware, pipettes, modules, volumes, and tip policies, with checks for Opentrons App analysis and operator review before physical runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft liquid-transfer or serial-dilution protocols
• Review deck layout, volumes, and tip changes
• Update older protocols for supported API versions

Build and operate bioinformatics workflows on Latch using Python, Nextflow, or Snakemake. Configure computing resources and input forms, move data, connect Registry records, and register, debug, launch, and monitor analysis runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create and register Python analysis workflows
• Package Nextflow or Snakemake pipelines for Latch
• Connect Latch Data and Registry records

Calculate seawater carbonate chemistry from two independent measurements using PyCO2SYS. Produce pH, carbonate species, and mineral saturation results, convert laboratory conditions to ocean conditions, and propagate measurement uncertainty while recording calculation assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate carbonate chemistry from paired seawater measurements
• Assess aragonite and calcite saturation
• Convert laboratory results to ocean temperature and pressure

Prepare and analyze genomic intervals, regions defined by chromosome coordinates, with Gtars. Validate BED files, compare overlaps and coverage, combine region sets, and plan tokenization or reference storage while tracking data provenance and resource limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating BED coordinates and reference assemblies
• Counting overlaps and comparing genomic region sets
• Converting genomic intervals into token IDs