AI & ML

browser-act Bundle

Extract structured review data from Google Maps search results using the BrowserAct API, enabling local business analysis, reputation monitoring, and competitive benchmarking.

browser-act Bundle

Extract full article contents from WeChat using the BrowserAct API, with keyword search and date filtering.

browser-act Bundle

Extract official websites and social media profiles (LinkedIn, Facebook, X/Twitter, Instagram, YouTube, TikTok) from a company name or website URL using automated browser scripts.

browser-act Bundle

Extracts structured data from Reddit posts and comments via the BrowserAct API for competitor analysis, brand sentiment tracking, and market research.

browser-act Bundle

Extracts YouTube video transcripts and metadata (title, publisher, likes) via the BrowserAct API without CAPTCHA or IP restrictions.

browser-act Bundle

Extract structured best-selling product data from Amazon, including titles, prices, ratings, reviews, sales volume, and promotions, using the BrowserAct API.

browser-act Bundle

Extract GitHub repository details and contributor contact information using keywords, star counts, and update dates via the BrowserAct API.

browser-act Bundle

Extracts YouTube video transcripts and metadata in batch via the BrowserAct API, using search keywords and date filters.

jimliu Bundle

Generates images using multiple AI providers including OpenAI, Google, Azure, and others. Supports text-to-image, reference images, aspect ratios, and batch generation from prompt files.

jimliu Bundle

Generates article cover images with customizable type, palette, rendering, text, and mood dimensions, supporting multiple aspect ratios and backends.

jimliu Bundle

Generates images and text via reverse-engineered Gemini Web API, supporting reference images and multi-turn conversations.

jimliu Bundle

Analyzes article structure, identifies positions requiring visual aids, and generates illustrations with consistent type, style, and palette.

k-dense-ai Bundle

Generate, predict, and embed protein sequences and structures using ESM3, ESMC, and ESMFold2 with local or cloud inference.

k-dense-ai Bundle

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

k-dense-ai Bundle

Design, simulate, and run quantum circuits on Google Quantum AI hardware and partner backends using Cirq.

k-dense-ai Bundle

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

k-dense-ai Bundle

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

k-dense-ai Bundle

Explain machine learning model predictions using SHAP values, compute feature importance, and generate visualizations including waterfall, beeswarm, bar, scatter, force, and heatmap plots.

k-dense-ai Bundle

Process and analyze large tabular datasets (billions of rows) that exceed available RAM using lazy, out-of-core DataFrames with fast aggregations, visualization, and machine learning integration.

k-dense-ai Bundle

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

k-dense-ai Bundle

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

k-dense-ai Bundle

Deploy and serve AI/ML models on Modal's serverless cloud platform with on-demand GPUs, autoscaling containers, persistent storage, and scheduled jobs.

k-dense-ai Bundle

Solve single and multi-objective optimization problems using NSGA-II/III, MOEA/D, and other evolutionary algorithms with customizable operators, constraint handling, and benchmark problems.

k-dense-ai Bundle

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

k-dense-ai Bundle

Parse FCS (Flow Cytometry Standard) files v2.0-3.1, extract events as NumPy arrays, read metadata and channels, and convert to CSV or DataFrame for flow cytometry data preprocessing.

k-dense-ai Bundle

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

k-dense-ai Bundle

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

k-dense-ai Bundle

Build and execute quantum circuits on IBM Quantum hardware, simulators, and third-party providers using the Qiskit framework.

k-dense-ai Bundle

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

k-dense-ai Bundle

Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.

k-dense-ai Bundle

Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.

k-dense-ai Bundle

Perform astronomical data analysis with Astropy: coordinate transformations, unit conversions, FITS I/O, cosmological calculations, time handling, table operations, and WCS transformations.

k-dense-ai Bundle

Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.

k-dense-ai Bundle

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

k-dense-ai Bundle

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.

k-dense-ai Bundle

Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.