K-Dense

Analyze pooled CRISPR screens from FASTQ reads or guide-count tables using MAGeCK. Validate inputs, assess library and replicate quality, and rank enriched or depleted genes with effect sizes and false discovery rates, which estimate false-positive proportions.

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• Counting guide reads from FASTQ files
• Checking library representation and replicate consistency
• Comparing treatment and control screens

Clean and compare tandem mass spectra, measurements used to study molecular fragments, with matchms. Standardize metadata, filter peaks, search reference libraries, and export ranked candidate matches with similarity scores and matched-peak counts.

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• Clean MS/MS spectra and standardize metadata
• Search reference libraries for compound candidates
• Compare spectra using similarity scores and matched peaks

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

Work with geographic points, lines, and areas in Python using GeoPandas. Guidance and local audit tools help check coordinate systems, geometry validity, spatial join matches, and export settings before trusting or sharing analysis results.

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• Checking geographic data before analysis
• Reviewing coordinate systems and transformations
• Auditing matches between points and areas

Organize ISO evidence for medical devices and laboratories, including controlled documents, risk records, corrective actions, and supplier controls. Produce structured gap reports for authorized human review while keeping certification, accreditation, and regulatory processes distinct.

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• Organize controlled documents and implementation records
• Check links in medical-device risk and design records
• Review corrective-action and supplier-control evidence

Extract text and its page coordinates locally from PDFs, Office documents, and images. Use OCR to read scans, export structured JSON or Markdown, process document folders, and render pages as PNG images for agents that analyze visual content.

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• Extracting text from scanned PDFs and images
• Preparing document text and coordinates for retrieval-based AI answers
• Batch-processing folders of research papers

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.

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

Turn supplied data or optional research into infographic drafts for reports, presentations, or social posts. Choose layouts and visual styles, generate PNG images, and use automated reviews to guide revisions while retaining review logs.

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• Turning report statistics into an infographic
• Creating a timeline of project milestones
• Comparing product features visually

Build, analyze, and visualize relationship data in Python with NetworkX. Find shortest routes, identify important nodes and groups, generate networks for simulations, and exchange graph data through tables and common file formats.

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• Finding important people and groups in social networks
• Calculating shortest routes in transportation networks
• Visualizing relationships between research papers

Convert PDF, Office, and other documents into Markdown that preserves useful text structure. Use batch conversion and literature organization to prepare files for search, text analysis, or AI systems that retrieve information from documents.

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• Convert PDF and Office files to Markdown
• Batch-convert a document folder
• Organize research papers with source metadata

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.

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

Turn small-molecule structures into numerical features for similarity searches and models that predict activity or properties. Using Molfeat, the skill covers representation choices, invalid inputs, alignment with experimental labels, and saving feature-generation settings for reuse.

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• Prepare molecular features for activity or property prediction
• Choose molecular representations for similarity searches
• Keep experimental labels aligned after rejecting invalid molecules

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.

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

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

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.

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

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

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• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Prepare experiments for Ginkgo Cloud Lab by choosing a protocol, organizing required inputs, checking configured pricing, and navigating browser ordering. Covers protein expression, RNA synthesis, stability assays, and setup of measurement methods.

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• Choosing a protein expression and purification protocol
• Preparing inputs for mRNA or circRNA synthesis
• Reviewing stability assays and measurement method setup

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.

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

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.

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

Find and download public cancer imaging data from NCI Imaging Data Commons. Select CT, MR, PET, or pathology datasets using metadata, view images in a browser, and check licenses and citations before using the data.

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• Find cancer images by disease, modality, or anatomical site
• Download selected DICOM image series
• View radiology and pathology images in a browser

Plan and audit HypoGeniC workflows that use language models to propose patterns from labeled text. Check configurations, dataset splits, cost limits, and saved predictions while keeping candidate hypotheses separate from scientific confirmation.

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• Reviewing task settings before a HypoGeniC run
• Checking duplicates across training and test data
• Estimating token and cost limits before model calls

Analyze candidate sites where sugars may attach to proteins, review experimental evidence, and prepare prediction or structural shielding workflows. Supports mutation planning, antibody glycan comparisons, and interpretation of site-specific measurements while distinguishing candidates from confirmed modifications.

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• Find candidate glycosylation sites in protein sequences
• Plan mutations and rescan altered sequences
• Compare antibody Fc glycoforms

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

Plan and write literature reviews using multiple scholarly databases. Document searches, screen papers, synthesize findings, and check citations, then prepare Markdown or PDF reports with transparent selection counts, evidence limitations, and reproducible search records.

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• Prepare a literature review for a thesis or research paper
• Record reproducible searches across scholarly databases
• Track screening decisions and exclusion reasons