「dataset」の検索結果

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

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Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility outputs.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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HuggingFace hf CLI: search/download/upload models, datasets.

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Analyzes a dataset of companies or profiles to identify market patterns. Use when the user provides a larger dataset and wants segments, dominant categories, gaps, and emerging patterns.

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Get structured data from catalogued providers instead of scraping pages. Firecrawl Alexandria is a library of official APIs, licensed publishers, and Firecrawl indexes (financial series, company data, government contracts and law, podcasts, listings, research). Use this skill when the user needs records, listings, prices, filings, transcripts, or a dataset, says "is there an API for", "find a data source for", "pull the records", "get the dataset", or when assembling the answer by scraping would take many pages that one provider call returns directly. Discover with search, inspect with find_tools, execute with scrape.

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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".

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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".

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DuckDBを使用した大規模データ分析の専門スキル。DuckDBのアーキテクチャ、SQL構文、パフォーマンス最適化、各種ファイル形式（CSV、Parquet、JSON等）の効率的な読み込み・書き出しを熟知。メモリ効率の良い分析、複雑なクエリの最適化、データパイプライン構築を支援。Use when analyzing large datasets, querying CSV/Parquet/JSON files directly, building data pipelines, or optimizing analytical queries with DuckDB.

Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)

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Turns an actuals-versus-budget extract (spreadsheet, export or pasted table) into a DRAFT variance table with absolute and percentage variances, favourable or adverse marking and a ranking by size, then drafts plain-language driver hypotheses for each material variance as questions to verify with a named owner, never as asserted causes. Use when the user asks to "explain these variances", "why are we over budget on this line", "write the variance commentary for the management pack", "actuals versus budget analysis" or "which lines drive the variance". Do not use for a dataset with no budget or comparison column, use dataset-insight-pack instead; for weekly operational figures against last week, use kpi-weekly-report-writer. Drafts for human review; never approves, authorises or signs off.

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Prepare and launch LoRA, LoKr, or T-LoRA training through lora-scripts-next (SD-Trainer), Anima-first. Use when the user asks to train a character, style, or concept LoRA, choose parameters or repeats and epochs, start a run, or monitor training on Anima, SD1.5, SDXL, or Flux. Organize and caption the dataset, run dataset-doctor, choose a documented starting preset from image count and VRAM, show one confirmation card, then call the mikazuki API and monitor its log.

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Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.

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Use when working with Cohere — cohere AI platform management covering models, embeddings, reranking, datasets, fine-tuning, and connectors. Use when monitoring API usage, analyzing model performance, reviewing fine-tuning jobs, managing datasets and connectors, or troubleshooting Cohere API issues.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Data product builder — Datasets, APIs, dashboards, reports, data marketplace listing

日本語の概要は準備中です。原文の説明を表示しています。

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

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Maintain weighted collections, datasets, groups, selectors, and random content tables.

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Gets actor and level 3D bounding boxes and projects them into a camera image as 2D boxes — the dataset-annotation workflow. Lists box geometry, draws 3D boxes in the world via debug, or captures one camera frame and writes an annotated PNG plus a JSON of 2D boxes for matching actors. Use when the user asks to "get bounding boxes", "draw boxes around the cars", "project boxes onto the camera", or "generate a detection dataset / annotations".

日本語の概要は準備中です。原文の説明を表示しています。