K-Dense

Create market research reports that connect claims to sources, compare competitors, and estimate market size. Build alternative forecasts with explicit assumptions, so readers can review the calculations, uncertainties, and evidence behind each conclusion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a market report for a new business
• Comparing market size estimates and their assumptions
• Building competitor comparisons with traceable sources

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

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, 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.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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 and check research documents for clinical decision-support evaluation, including evidence profiles, cohort tables, survival plans, and model assessments. Work with aggregate or synthetic inputs, record sources and review status, and keep outputs outside patient care.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing research model evaluation documents
• Checking evidence profiles and source links
• Reviewing survival analysis plans

Plan and review fluid simulations with FluidSim, from solver selection and resource estimates to output analysis and restart checks. Examine numerical accuracy through grid and time-step comparisons, conservation checks, and explicit limits on computing resources.

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• Selecting a solver and reviewing simulation parameters
• Estimating memory, disk space, and runtime needs
• Inspecting spectra and conservation budgets

Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Read and write h5ad or Zarr datasets
• Combine experimental batches with source labels
• Select cells or genes using metadata

Explore difficult decisions, research trade-offs, or creative challenges through several simulated perspectives. Separate facts, assumptions, and values, compare competing priorities, and produce a conditional recommendation with unresolved questions, a next step, and conditions that would change the choice.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing business options with competing priorities
• Reviewing trade-offs in a research plan
• Challenging assumptions behind a preferred choice

Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding promoter regions in a DNA sequence
• Predicting splice donor and acceptor sites
• Estimating gene expression for a specified cell type

Review supplied writing through twelve DHDNA dimensions, a framework for describing reasoning and expression. Receive observations supported by quotations, optional scores, and comparisons between samples, with clear limits separating textual patterns from psychological traits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reflect on reasoning patterns in your writing
• Compare argumentation across selected texts
• Review how a text expresses assumptions and uncertainty

Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.

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• Importing DNA sequences from FASTA files
• Auditing sample inventory at a storage location
• Exporting sequences for a specific schema to CSV

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Generate photos, illustrations, logos, and presentation visuals through the OpenRouter Image API. Edit or combine reference images, choose among supported models, and check available settings such as resolution, output format, and transparency before generation.

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• Creating illustrations and visuals for slides or posters
• Editing colors or styles in existing images
• Combining multiple reference images

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

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

Inspect FCS files from flow cytometry, a method for measuring cell properties, using FlowIO. Extract measurement data into NumPy arrays, review channel metadata, export tables, and create FCS 3.1 files for downstream work.

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• Inspect measurement settings and channel labels in FCS files
• Extract measurement data into NumPy arrays or CSV tables
• Read legacy FCS files containing multiple datasets

Plan experiments before collecting data. Helps choose study designs, randomly assign subjects or samples, organize treatment combinations, and generate reproducible schedules while checking for confounding and repeated measurements incorrectly counted as independent samples.

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• Create random assignments for subjects or samples
• Screen multiple experimental factors
• Balance conditions across batches, days, or plate positions

Analyze flow cytometry data in Python by correcting fluorescence spillover and applying gates that select cell populations. Produce population counts, percentages, and fluorescence summaries, or reproduce supported FlowJo 10 workspace analyses with recorded analysis settings.

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• Applying the same gating strategy to multiple FCS samples
• Reporting cell-population counts and percentages with denominators
• Reproducing supported FlowJo 10 analyses in Python

Retrieve public evidence and linked publications for a supported genetic variant, or look up gene–disease relationships. Preserve sources, conflicting assertions, and explicit search outcomes so qualified professionals can review the findings.

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• Review public evidence for a single genetic variant
• Find publications linked to a resolved variant
• Look up gene–disease relationships

Analyze DepMap cancer model data to rank genes required for cell survival, compare groups defined by biological markers, and explore drug responses. Release tracking and statistical checks help organize evidence for research hypotheses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Ranking gene dependencies in cancer models
• Comparing groups defined by biological markers
• Finding genes with similar dependency patterns

Analyze existing evolutionary trees and other hierarchical trees with ETE 4. Edit branches, add annotations, compare branching structures, explore large trees interactively, and render figures for publication using the appropriate optional visualization components.

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• Prune selected species and change a tree's root
• Compare branching structures between two trees
• Add NCBI or GTDB taxonomy information