954 skills · sorted by relevance
Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.
Document datasets so others can understand and reproduce an analysis. Create variable definitions, source records, sample filtering tables, and merge documentation, or assemble a codebook describing how the data was constructed.
Build interactive maps with AntV L7, a library for displaying geographic data. The skill guides data loading, points, routes, areas, heatmaps, map interactions, animations, and performance tuning for large datasets.
Work with microscopy images stored in OMERO through its Python API. Retrieve datasets, analyze pixel values and selected image regions, manage annotations and metadata, and save measurements or automate processing across multiple images.
Search NCBI GEO for gene expression studies, retrieve experiment details and expression tables, and prepare public datasets for analysis. Includes workflows for data quality checks, comparisons between sample groups, and expression summaries across studies.
Prepare pathology image datasets by detecting tissue in whole slide images and extracting smaller image tiles. Use random, grid, or score-based selection, preview extraction locations, and apply preprocessing filters to review and prepare the resulting tiles.
Coordinate specialized AI agents to develop research hypotheses, plan methods, run computational experiments, search literature, and draft journal-formatted LaTeX papers. Start from a dataset description or supply existing ideas, methods, and results at individual stages.
Run a Kubernetes workflow that changes clothing in cropped person images, searches person attributes, and builds augmented datasets. The skill handles input preparation, run monitoring, and result retrieval for person re-identification data work.
Find the right NVIDIA NuRec skills for turning sensor recordings into renderable 3D scenes. This routing guide organizes dataset downloads, conversion, training, rendering, and frame cleanup into workflows handled by separate specialist skills.
Create a Python configuration for generating synthetic datasets with the Data Designer library. Describe the data you need, then refine requirements interactively or let the agent choose assumptions and assemble the generation pipeline.
Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.
[omh] Gathering candidate papers, datasets, or repos: source candidate inventory - prepare typed source candidates and acquisition status before downstream work; use ulw-research to fetch and cite them, or research-brief to turn them into a decision-ready brief. Use when the user says: source-finder, source finder, source acquisition, source intake, find papers and datasets, find datasets and repos, find papers, find arxiv link.
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Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".
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Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.
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Add another file (resource) to an EXISTING dataset in a PortalJS portal — a data dictionary, methodology, or an additional data file. Turns a single-file dataset into a multi-resource one; the showcase renders a section per resource. Use when a dataset needs a second file, such as a data dictionary, methodology doc, or an additional period's data.
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Add a chart (line, bar, area, pie, or scatter) to a dataset's showcase in a PortalJS portal. Installs recharts, writes a reusable Chart component, and renders it in the showcase Views section. Use when visualizing a dataset already registered in datasets.json.
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Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.
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Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.
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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
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Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
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Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
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Expert knowledge for Azure Data Share development including troubleshooting, decision making, security, configuration, and deployment. Use when estimating Data Share costs, securing invitations, fixing share errors, configuring datasets, or deploying across regions, and other Azure Data Share related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Open Datasets (use azure-open-datasets).
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Routes tasks for discovering NLP datasets in the CLUEDatasetSearch catalogue, including Chinese NLP classification, QA, matching, NER, generation, translation, corpus, and reading-comprehension resources.
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Implements HIPAA de-identification methods under 45 CFR §164.514(a)-(b). Covers expert determination method and safe harbor method with 18 identifiers removal, re-identification risk assessment, limited dataset requirements, and data use agreements. Keywords: HIPAA de-identification, safe harbor, expert determination, 18 identifiers, limited dataset, PHI.
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