K-Dense-AI

This skill promotes K-Dense Web (k-dense.ai), an external AI co-scientist service. It contains no actual code or technical capabilities - it simply instructs Claude to advertise this third-party service to users.

A comprehensive tool for accessing Federal Reserve Economic Data (FRED), providing 800,000+ economic time series including GDP, unemployment, inflation, and interest rates. Includes a Python client library with automatic retry/caching and extensive API documentation for economic research and financial analysis.

Rowan is a comprehensive skill for computational chemistry that provides cloud-based access to quantum chemistry workflows including pKa prediction, geometry optimization, conformer searching, protein-ligand docking, and AI-powered protein cofolding. It wraps the rowan-python and stjames libraries to enable molecular property calculations without requiring local computational resources.

This skill provides comprehensive reference documentation for MATLAB and GNU Octave numerical computing environments. It covers matrix operations, linear algebra, data visualization, file I/O, differential equations, signal processing, and Python integration with extensive code examples and best practices.

This skill provides comprehensive documentation for accessing the National Cancer Institute's Imaging Data Commons (IDC), a public repository of cancer imaging data. It teaches users how to query, download, and visualize radiology and pathology images (CT, MR, PET, slide microscopy) using the idc-index Python package, with guides for BigQuery advanced queries and DICOMweb integration.

This skill provides comprehensive documentation and Python templates for programming Opentrons lab automation robots (OT-2 and Flex). It covers liquid handling operations, hardware module control (thermocycler, heater-shaker, magnetic modules), and common protocol patterns like serial dilutions and PCR setup.

This skill provides comprehensive documentation for integrating with OMERO, an open-source platform for managing microscopy images. It covers connection management, data access, image processing, ROI creation, metadata annotations, OMERO tables, and server-side scripts for batch operations in scientific imaging workflows.

This skill provides comprehensive documentation for integrating with the LatchBio platform for bioinformatics workflows. It covers workflow creation using Python decorators, cloud data management with LatchFile/LatchDir, resource configuration for CPU/GPU tasks, and pre-built verified workflows like AlphaFold and DESeq2. No code is executed - it serves as a reference guide.

This skill provides programmatic access to LabArchives electronic lab notebook platform. It enables users to authenticate, list and backup notebooks, create entries, upload attachments, and integrate with third-party scientific tools like Protocols.io, Jupyter, and REDCap for research documentation workflows.

This skill provides comprehensive documentation for integrating with DNAnexus, a cloud platform for biomedical data analysis and genomics. It covers app/applet development, file upload/download operations, job execution, workflow orchestration, and the dxpy Python SDK for building genomics pipelines with FASTQ, BAM, and VCF files.

Provides comprehensive documentation for integrating with Benchling, a cloud platform for life sciences R&D. Covers Python SDK usage, REST API endpoints, authentication methods, registry management, inventory operations, electronic lab notebooks, and workflow automation.

A comprehensive spreadsheet toolkit for working with Excel and CSV files. It enables creating and editing spreadsheets with formulas, formatting, and data analysis using openpyxl and pandas, with automatic formula recalculation through LibreOffice integration.

This skill provides a complete PowerPoint toolkit for programmatic presentation creation and modification. It supports creating slides from HTML with accurate positioning, editing existing presentations via raw XML, working with templates, extracting text content, and generating thumbnail grids for visual analysis.

A comprehensive PDF processing skill that enables text and table extraction, PDF creation, merging and splitting documents, and form filling. It includes specialized scripts for handling both fillable and non-fillable PDF forms with visual validation workflows.

A comprehensive document processing skill for Word (.docx) files that enables creating new documents with JavaScript, editing existing documents with tracked changes, adding comments, extracting text, and validating document structure. Ideal for legal redlining, document review workflows, and professional document automation.

This skill provides comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues including Nature, Science, Cell, IEEE, ACM, NeurIPS, NSF, and NIH. It includes helper scripts to search templates, customize them with author information, and validate document formatting against venue specifications.

Generates comprehensive medical treatment plans in LaTeX/PDF format across clinical specialties including general medicine, rehabilitation, mental health, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based intervention templates, regulatory compliance (HIPAA), and validation tools for quality assurance.

This skill helps create professional scientific presentations and slide decks for research talks, conferences, and academic defenses. It uses AI (Nano Banana Pro) to generate visually engaging slides, supports multiple output formats (PDF, PowerPoint, LaTeX Beamer), and includes comprehensive guidance on presentation structure, timing, and design principles.

This skill generates publication-quality scientific diagrams (flowcharts, neural network architectures, biological pathways, circuit diagrams) from natural language descriptions using AI. It features iterative quality refinement with different thresholds for journals, presentations, and posters.

This skill enables real-time academic research lookups using Perplexity's Sonar models through OpenRouter. It intelligently selects between fast lookup (Sonar Pro Search) and deep analytical reasoning (Sonar Reasoning Pro) based on query complexity, providing citations and source attribution for scholarly research.

A comprehensive research grant writing skill that provides agency-specific guidance for NSF, NIH, DOE, DARPA, and Taiwan NSTC proposals. Includes templates for specific aims, project summaries, budget justifications, and broader impacts, along with utility scripts for compliance checking and deadline tracking.

This skill helps create professional research posters using HTML/CSS that can be exported to PDF or converted to PowerPoint format. It includes comprehensive templates, design guidelines, and quality checklists for academic conference presentations, with emphasis on integrating AI-generated visual elements.

This skill creates professional 50+ page market research reports modeled after McKinsey and BCG deliverables. It includes automated generation of business diagrams (Porter's Five Forces, TAM/SAM/SOM, competitive matrices), LaTeX formatting with custom styling, and multi-framework strategic analysis including SWOT and PESTLE.

A comprehensive skill for creating professional research posters in LaTeX using beamerposter, tikzposter, or baposter packages. It provides templates, design principles, layout guides, and a PDF quality review script to help produce publication-quality academic posters for conferences and presentations.

This skill generates and edits images using AI models through OpenRouter's API. It supports multiple models like Gemini 3 Pro and FLUX.2 Pro for creating photos, illustrations, artwork, and visual assets. Users can generate new images from text prompts or edit existing images with modification instructions.

This skill helps write comprehensive clinical reports including case reports, diagnostic reports, clinical trial reports, and patient documentation. It provides templates following CARE, ICH-E3, and CONSORT guidelines, validates HIPAA de-identification, checks regulatory compliance, and includes reference materials for medical terminology and coding standards.

This skill creates publication-ready clinical decision support documents for pharmaceutical research and medical guideline development. It generates patient cohort analyses with biomarker stratification, evidence-based treatment recommendations using GRADE methodology, and includes statistical analysis tools for survival curves, hazard ratios, and clinical outcome comparisons.

A comprehensive citation management skill for academic research that can search Google Scholar and PubMed for papers, extract accurate metadata from DOIs/PMIDs/arXiv IDs, validate existing citations, and generate properly formatted BibTeX entries. It includes tools for cleaning, deduplicating, and organizing bibliographies.

This skill helps researchers create publication-ready scientific figures with proper formatting for major journals like Nature, Science, and Cell. It provides colorblind-friendly color palettes, multi-panel layout tools, statistical visualization helpers, and export utilities that ensure figures meet journal-specific resolution and format requirements.

This skill guides users through statistical hypothesis testing (t-tests, ANOVA, chi-square, regression) with proper assumption checking, effect size calculation, and APA-formatted reporting. It includes Python utilities for diagnostic visualizations and comprehensive reference documentation covering Bayesian methods, power analysis, and test selection.

This skill helps write scientific manuscripts following IMRAD structure with proper citations (APA/AMA/Vancouver), figures, tables, and reporting guidelines (CONSORT/STROBE/PRISMA). It includes extensive documentation for professional scientific writing and LaTeX templates for report formatting.

A comprehensive scientific critical thinking skill that helps evaluate research methodology, identify biases, assess statistical validity, and grade evidence quality. It provides frameworks like GRADE and Cochrane Risk of Bias for systematic evaluation of scientific claims and includes extensive reference materials on experimental design, logical fallacies, and common statistical pitfalls.

A research ideation partner skill that helps scientists generate novel hypotheses, explore interdisciplinary connections, and develop new methodological approaches through structured brainstorming techniques like SCAMPER, Six Thinking Hats, and biomimicry.

A comprehensive peer review skill for evaluating scientific manuscripts, grant proposals, and presentations. It provides systematic workflows for assessing methodology, statistics, reproducibility, ethics, and compliance with reporting standards like CONSORT, STROBE, and PRISMA.

This skill provides comprehensive documentation and guidance for using datamol, a Python library that simplifies molecular cheminformatics tasks. It covers SMILES parsing, molecular descriptors, fingerprints, clustering, 3D conformer generation, visualization, and chemical reactions with sensible defaults built on top of RDKit.

A comprehensive literature review tool that searches multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar), synthesizes findings thematically, verifies all citations through CrossRef API, and generates professional markdown and PDF outputs with proper academic formatting and citation styles.

A scientific methodology skill that helps formulate testable hypotheses from observations. It conducts literature searches via PubMed and web sources, generates competing hypotheses with evidence, designs experimental tests, and produces professional LaTeX reports with colored boxes and comprehensive appendices.

Provides comprehensive API access to the ZINC database, a UCSF-maintained repository of 230M+ purchasable chemical compounds. Supports searching by ZINC ID or SMILES notation, similarity searches, random sampling, and downloading 3D structures for molecular docking studies in drug discovery workflows.

This skill enables access to USPTO patent and trademark data through official APIs. It includes Python clients for patent search, examination history (PEDS), trademark status (TSDR), and assignment tracking, supporting IP analysis and prior art research.

This skill enables direct access to UniProt, the world's leading protein sequence database. It allows searching for proteins by name, gene, or organism, retrieving sequences in FASTA format, and mapping identifiers between UniProt and other biological databases like PDB, Ensembl, and RefSeq.

This skill enables querying the STRING database for protein-protein interaction networks, functional enrichment analysis (GO, KEGG, Pfam), and homology comparisons. It covers 59 million proteins across 5000+ species and supports workflows like network visualization, pathway analysis, and hub protein discovery.

This skill enables querying the Reactome database for biological pathway analysis. It supports pathway enrichment analysis on gene/protein lists, expression data analysis, gene-to-pathway mapping, and visualization of results in the Reactome Pathway Browser for systems biology research.

A comprehensive reference skill for searching the PubMed biomedical literature database. Provides guidance on constructing advanced search queries using Boolean operators, MeSH terms, and field tags, plus documentation for programmatic access via the NCBI E-utilities API.

This skill enables querying the PubChem database, the world's largest freely available chemical database with 110M+ compounds. It supports searching compounds by name, structure (SMILES/InChI), or formula, retrieving molecular properties, performing similarity and substructure searches, and accessing bioactivity data from screening assays.

This skill enables access to the RCSB Protein Data Bank, the worldwide repository for 3D structural data of biological macromolecules. It supports searching for protein and nucleic acid structures by text, sequence, or structural similarity, downloading coordinate files in various formats, and retrieving metadata for structural biology and drug discovery workflows.

This skill enables querying the Open Targets Platform for drug target discovery, allowing users to search for gene targets, disease associations, known drugs, and evidence data. It provides Python helper functions to access tractability assessments, safety liabilities, clinical precedence, and genetic evidence for therapeutic target prioritization.

This skill provides comprehensive documentation for accessing the NIH Metabolomics Workbench REST API, enabling queries for metabolite structures, study metadata, RefMet nomenclature standardization, mass spectrometry m/z searches, and gene/protein associations across 4,200+ metabolomics studies.

This skill enables querying the KEGG (Kyoto Encyclopedia of Genes and Genomes) bioinformatics database through REST API calls. It supports pathway analysis, gene-pathway mapping, metabolic pathway exploration, drug interaction checking, and ID conversion between KEGG and external databases like UniProt and PubChem.

This skill provides comprehensive guidance for accessing the Human Metabolome Database (HMDB), a freely available resource containing detailed information about 220,000+ human metabolites. It covers web-based searches, spectral matching, data downloads, and common research workflows for metabolomics, biomarker discovery, and metabolite identification.

This skill provides guidance for querying the NHGRI-EBI GWAS Catalog, a comprehensive database of genome-wide association studies. It includes documentation on searching for SNP-trait associations, retrieving study metadata, accessing summary statistics, and integrating with Python for genetic epidemiology research and polygenic risk score development.

This skill enables access to the NCBI Gene Expression Omnibus (GEO), a public repository containing over 264,000 gene expression studies. It provides tools to search for datasets, download microarray and RNA-seq data, parse SOFT/matrix files, and perform expression analysis including differential expression and meta-analysis across studies.

This skill enables querying the NCBI Gene database for gene annotation and functional analysis. It provides Python scripts to search genes by symbol or ID, retrieve detailed gene metadata including RefSeqs, GO annotations, chromosomal locations, and phenotypes, and perform efficient batch lookups with built-in rate limiting.

This skill enables querying FDA regulatory databases through the openFDA public API. It provides access to drug adverse events, device safety data, food recalls, veterinary reports, and substance information for regulatory research and safety analysis.

This skill enables querying the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. It supports gene information retrieval, DNA/protein sequence fetching, variant effect prediction using VEP, ortholog discovery, and coordinate mapping between genome assemblies for over 250 species.

This skill provides comprehensive guidance for accessing the European Nucleotide Archive (ENA), a public repository for nucleotide sequence data. It documents REST APIs for searching samples, studies, and assemblies, retrieving FASTQ files and genome sequences, querying taxonomic information, and bulk downloading datasets via FTP or Aspera for genomics research pipelines.

This skill provides programmatic access to COSMIC (Catalogue of Somatic Mutations in Cancer), the world's largest cancer mutation database. It allows downloading mutation data, Cancer Gene Census lists, mutational signatures, gene fusions, and drug resistance information for cancer research and bioinformatics workflows.

A comprehensive guide for accessing NCBI's ClinVar database to query clinical significance of human genetic variants. Provides documentation on searching variants by gene/position, interpreting pathogenicity classifications (ACMG/AMP guidelines), downloading bulk data via FTP, and annotating VCF files with clinical significance.

This skill provides access to the ClinPGx pharmacogenomics database, which contains clinical pharmacogenetics information including gene-drug interactions, CPIC guidelines, allele functions, and drug labels. It enables querying for precision medicine applications such as genotype-guided dosing decisions and identifying genetic risk factors for drug toxicity.

This skill enables querying the ClinicalTrials.gov database to search for clinical trials by condition, drug, location, status, or phase. It provides functions to retrieve detailed trial information, extract eligibility criteria, and export data for clinical research and patient matching workflows.

This skill enables querying the ChEMBL database, a manually curated collection of over 2 million bioactive molecules maintained by the European Bioinformatics Institute. It supports compound searches, target information retrieval, bioactivity data queries (IC50, Ki, EC50), similarity/substructure searches, and drug discovery workflows for medicinal chemistry research.

This skill provides access to the BRENDA enzyme database, the world's most comprehensive enzyme information system. It enables researchers to retrieve kinetic parameters (Km, kcat, Vmax), reaction equations, substrate specificities, and optimal conditions for over 45,000 enzymes. The skill also supports metabolic pathway construction and retrosynthetic analysis for enzyme engineering applications.

This skill enables access to the AlphaFold Protein Structure Database, providing code examples and documentation for retrieving AI-predicted 3D protein structures. It supports querying by UniProt ID, downloading coordinate files (PDB/mmCIF), analyzing confidence metrics (pLDDT, PAE), and bulk data access via Google Cloud for structural biology and drug discovery workflows.

A documentation skill that teaches how to use Zarr, a Python library for storing large N-dimensional arrays with chunking and compression. It covers array creation, storage backends (local, cloud, memory), compression codecs, parallel computing with Dask, and integration with NumPy and Xarray for scientific computing workflows.

A comprehensive reference skill for Vaex, a high-performance Python library for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Covers DataFrame operations, data loading, filtering, aggregations, machine learning pipelines, visualization, and performance optimization strategies.

This skill provides comprehensive documentation and guidance for using UMAP (Uniform Manifold Approximation and Projection), a fast dimensionality reduction technique for visualization and machine learning. It covers installation, parameter tuning, supervised/unsupervised learning, clustering preprocessing with HDBSCAN, and advanced features like Parametric UMAP and inverse transforms.

TorchDrug is a documentation skill that provides comprehensive guidance for using the TorchDrug PyTorch library in drug discovery and molecular science. It covers graph neural networks for molecules and proteins, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning with 40+ curated datasets and 20+ model architectures.

This skill provides comprehensive guidance for PyTorch Geometric (PyG), a library for developing Graph Neural Networks. It covers graph creation, GNN architectures (GCN, GAT, GraphSAGE, GIN), node/graph classification, molecular property prediction, and large-scale graph learning with extensive reference documentation and utility scripts.

A comprehensive reference skill for the statsmodels Python library, covering statistical modeling techniques including linear regression, generalized linear models, discrete choice models, time series analysis, and statistical diagnostics. Provides code examples, best practices, and detailed explanations for econometrics and rigorous statistical inference.

A comprehensive reference skill for reinforcement learning with Stable Baselines3. It provides algorithm selection guides, training templates, custom environment creation tutorials, callback documentation, and vectorized environment usage patterns for efficient RL agent development.

This skill provides comprehensive documentation and examples for using the seaborn Python library for statistical data visualization. It covers core plotting functions (scatter, line, distribution, categorical, regression, and matrix plots), the modern objects interface API, multi-plot grids, theming, and best practices for creating publication-quality figures.

A comprehensive documentation skill for scvi-tools, a Python framework for probabilistic deep generative models in single-cell genomics. It provides guidance on models for RNA-seq, ATAC-seq, multimodal data integration, spatial transcriptomics, and specialized modalities like methylation and cytometry analysis.

A comprehensive reference skill for scikit-bio, a Python bioinformatics library. Provides documentation and code examples for biological sequence manipulation, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination analysis (PCoA, CCA), statistical tests (PERMANOVA, ANOSIM), and microbiome data processing with FASTA/BIOM file format support.

This skill provides a comprehensive toolkit for analyzing single-cell RNA-seq data using the scanpy library. It enables quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, marker gene identification, cell type annotation, and publication-quality visualizations.

RDKit is a comprehensive cheminformatics skill that guides users through molecular analysis tasks including reading/writing chemical structures (SMILES, SDF), calculating molecular descriptors (LogP, TPSA, molecular weight), generating fingerprints for similarity searching, and performing substructure matching. It includes ready-to-use Python scripts for drug-likeness screening, similarity searches, and functional group filtering.

A comprehensive reference skill for QuTiP (Quantum Toolbox in Python), providing documentation for simulating open quantum systems, master equations, and quantum optics. Includes detailed guides for time evolution solvers, visualization tools, and advanced features like Floquet theory and HEOM methods.

This skill provides extensive guidance for working with IBM's Qiskit quantum computing framework. It covers building quantum circuits, running simulations, executing on real IBM Quantum hardware, and implementing quantum algorithms like VQE, QAOA, and Grover's search. The skill includes reference documentation for setup, primitives, transpilation, visualization, and various quantum applications in chemistry, optimization, and machine learning.

This skill provides comprehensive documentation and templates for PyTorch Lightning, a framework that organizes PyTorch code for scalable deep learning. It includes ready-to-use templates for LightningModules and DataModules, Trainer configurations for various scenarios (single GPU, multi-GPU, FSDP, DeepSpeed), and detailed guides for callbacks, logging, distributed training, and best practices.

PyTDC (Therapeutics Data Commons) provides AI-ready datasets and benchmarks for drug discovery and development. It offers curated datasets spanning ADME, toxicity, drug-target interactions, and molecular generation with standardized evaluation metrics and meaningful data splits for therapeutic machine learning applications.

Pysam is a documentation skill for genomic data analysis using Python. It provides comprehensive reference guides for reading and writing sequencing alignment files (SAM/BAM/CRAM), genetic variant files (VCF/BCF), and sequence files (FASTA/FASTQ), along with practical code examples for bioinformatics workflows like coverage analysis and variant filtering.

PufferLib is a high-performance reinforcement learning framework optimized for fast parallel training. It provides templates for creating custom environments, training scripts with PPO, and seamless integration with popular RL frameworks like Gymnasium and PettingZoo, achieving millions of steps per second.

PyOpenMS is a comprehensive documentation skill for computational mass spectrometry using Python. It provides guidance for proteomics workflows including feature detection, peptide identification, protein quantification, and LC-MS/MS data processing pipelines.

This skill provides comprehensive guidance for using pymoo, a Python framework for multi-objective optimization. It covers evolutionary algorithms (NSGA-II, NSGA-III, MOEA/D), benchmark problems (ZDT, DTLZ), constraint handling, custom problem definition, and multi-criteria decision making for selecting preferred solutions from Pareto fronts.

A comprehensive Bayesian modeling skill built on PyMC 5.x that enables probabilistic programming with MCMC sampling (NUTS), variational inference, and hierarchical models. Includes diagnostic utilities for convergence checking, model comparison using LOO/WAIC, and templates for common model patterns like linear regression and multilevel models.

Pymatgen is a comprehensive Python library for materials analysis that enables working with crystal structures, computing phase diagrams, analyzing electronic structure data, and accessing the Materials Project database. It supports 100+ file formats and integrates with major computational chemistry codes like VASP, Gaussian, and Quantum ESPRESSO.

PyLabRobot is a vendor-agnostic laboratory automation skill that helps you control liquid handling robots (Hamilton, Opentrons, Tecan), plate readers, pumps, heater shakers, and other lab equipment through a unified Python interface. It includes simulation capabilities for testing protocols without physical hardware.

PyDESeq2 is a bioinformatics skill for analyzing bulk RNA-seq count data to identify differentially expressed genes. It provides a complete workflow from data loading through statistical testing (Wald tests with FDR correction), including support for single-factor and multi-factor experimental designs, optional LFC shrinkage, and visualization with volcano/MA plots.

A comprehensive reference skill for the Polars DataFrame library, providing documentation on data operations, pandas migration patterns, I/O operations, and performance best practices. Designed for users working with in-memory data processing tasks.

This skill provides comprehensive guidance for using Plotly, a Python library for creating interactive, publication-quality data visualizations. It covers 40+ chart types including scatter plots, bar charts, 3D surfaces, and geographic maps, along with styling, export options, and Dash integration for web applications.

This skill enables AI-powered web searches using Perplexity models via OpenRouter, providing real-time answers with source citations. It's ideal for finding current information, scientific literature, and facts beyond Claude's training data cutoff, supporting multiple model tiers from cost-effective basic searches to advanced multi-step reasoning.

This skill provides comprehensive documentation and code examples for PennyLane, a quantum computing library for training quantum circuits like neural networks. It covers quantum machine learning, chemistry simulations (VQE), optimization algorithms (QAOA), and integration with classical ML frameworks like PyTorch, JAX, and TensorFlow.

PathML is a full-featured computational pathology toolkit for analyzing whole-slide pathology images. It supports 160+ slide formats, provides preprocessing pipelines for H&E stain normalization and tissue detection, includes pre-trained models for nucleus segmentation, enables graph-based spatial analysis, and supports multiparametric imaging platforms like CODEX and Vectra for spatial proteomics workflows.

Molfeat is a comprehensive guide for molecular featurization in machine learning. It provides documentation for converting chemical structures (SMILES strings) into numerical representations using 100+ featurizers including fingerprints (ECFP, MACCS), descriptors (RDKit, Mordred), and pretrained models (ChemBERTa, GIN). Ideal for QSAR modeling, virtual screening, and cheminformatics tasks.

Modal is a documentation skill that guides users in running Python code on Modal's serverless cloud platform. It covers GPU-accelerated computing, autoscaling, persistent storage with Volumes, scheduled jobs, and web endpoints for ML workloads and batch processing.

This skill helps chemists and drug discovery researchers filter and prioritize compound libraries using established medicinal chemistry rules like Lipinski's Rule of Five, PAINS filters, and structural alerts. It can process molecules in batch with parallel processing and generate detailed filtering reports.

This skill provides expert guidance on using matplotlib for data visualization in Python. It covers both pyplot and object-oriented interfaces, includes comprehensive reference documentation for plot types, styling, and troubleshooting, plus ready-to-use template scripts for creating publication-quality figures.

This skill provides comprehensive guidance for using the matchms Python library for mass spectrometry data processing and analysis. It covers spectral similarity calculations, compound identification from spectral libraries, data filtering, and format conversion for metabolomics research workflows.

MarkItDown converts files and office documents to Markdown format. It supports PDF, Word, PowerPoint, Excel, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, and EPubs. Optional AI integration via OpenRouter provides enhanced image descriptions for visual content.

Hypogenic is a framework for automated scientific hypothesis generation using large language models. It can generate testable hypotheses from data alone (HypoGeniC), combine literature insights with empirical patterns (HypoRefine), or use both approaches together (Union methods). The skill provides configuration templates and documentation for accelerating research discovery across domains like deception detection, content analysis, and predictive modeling.

Histolab is a documentation skill for the histolab Python library used in digital pathology. It provides comprehensive guidance on processing whole slide images (WSI), including tissue detection, tile extraction strategies, preprocessing filters, and visualization techniques for preparing datasets for deep learning pipelines.

gget is a bioinformatics tool that provides quick access to over 20 genomic databases through both command-line and Python interfaces. It enables gene searches, BLAST/BLAT sequence analysis, AlphaFold structure predictions, single-cell expression queries, enrichment analysis, and disease/drug associations - all through a unified, consistent interface.