Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.
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.
Convert genomic positions between file conventions, standardize variant representations, and check reference genome or chromosome naming conflicts. The skill helps investigate one-base shifts and compare individual variants against a reference sequence before combining analysis files.
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.
Retrieve and combine protein annotations, biological pathways, and chemical identifiers from services such as UniProt, KEGG, and UniChem using Python. Supports cross-database lookups and batch identifier conversion while preserving multiple matches and failed requests.
Manage scientific datasets with DataLad and git-annex, which tracks large files separately from Git history. Retrieve selected data, record analysis commands and their inputs and outputs, rerun recorded work, and publish history alongside file content.
Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.
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.
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.
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.
Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery 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.
Query public single-cell and spatial gene expression data by tissue, disease, or cell type. Retrieve selected expression values, cell metadata, and summary counts for analysis and comparisons across datasets without first downloading entire collections.
Implement and debug astronomical data analysis with Astropy in Python. Work with physical units, celestial coordinates, observation files, tables, precise time conversions, and cosmological models to prepare data and calculate astronomical quantities.
Build molecular property prediction workflows with DeepChem for solubility, toxicity, and related chemistry tasks. Prepare molecular representations, choose compatible models, handle missing labels, and evaluate predictions using data splits designed for the intended scientific question.
Check a Bot’s connection to Hindsight and set up dedicated and shared memory stores. The skill also creates a user knowledge summary when needed and reports which other memory stores are available to read.
Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.
Plan how an AI application should use Hindsight memory. Identify useful information to retain, define user-scoping tags and memory bank settings, and specify how retrieved facts and synthesized knowledge should reach the model.
Build Python image analysis with Azure AI Vision to generate captions and tags, locate objects and people, and extract text using OCR, or optical character recognition. It also provides suggested crop regions.
Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.
Build Python features that identify a speaker’s intent and extract details such as names from conversation text using Azure. Get guidance on request structure, authentication, client cleanup, and exception handling.
Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.
Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.
Helps implement document processing in TypeScript with Azure, extracting text, tables, invoice fields, and receipt details. Covers ready-made models, custom model training, document classification, authentication, and waiting for analysis results.