494 件 ・ 関連度順
概要と使いどころ
PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.
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Use DIG (Dive into Graphs) to load graph-learning datasets, run graph generation, self-supervised learning, GNN explainability, 3D graph learning, GOOD OOD datasets, graph augmentation, fair graph learning, and large-scale graph workflows.
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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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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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Switch this session to another Cognee dataset. Lists the datasets you can write to, lets you pick one, syncs the current session into its dataset, then starts a fresh Cognee session bound to the chosen dataset. All later saves and recalls target the new dataset and the status line shows it.
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Use when the user wants to switch this Codex session to another Cognee dataset, see which datasets are available, or move memory capture to a different dataset. Syncs the current session first, then starts a fresh Cognee session bound to the chosen dataset.
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Investigate datasets and resolve entities using OpenPlanter methodology — cross-reference heterogeneous sources, build Admiralty/ACH confidence-tiered evidence chains, and apply OSINT tradecraft. Triggers on entity resolution, cross-reference datasets, evidence chain, OSINT investigation, structured investigation.
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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 ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.
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Search, query, and cross-reference NCBI GEO (Gene Expression Omnibus) datasets with ENCODE experiments. Use when the user wants to find GEO accessions for ENCODE experiments, search GEO for complementary datasets, download GEO metadata or series matrices, cross-reference ENCODE and GEO data, find supplementary files from GEO, or link GEO series to ENCODE experiments for provenance tracking. Also use when the user mentions GEO, GSE, GSM, GPL, GDS, series matrix, SOFT format, or needs to find expression data in GEO that complements their ENCODE analysis.
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Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
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Use when deciding whether Salesforce Reports, CRM Analytics, or Tableau is the right level of analytics tooling, and when reviewing or troubleshooting basic CRM Analytics designs. Triggers: 'CRM Analytics', 'Einstein Analytics', 'Tableau CRM', 'lens', 'dataset', 'dataflow', 'analytics dashboard', 'license requirement'. NOT for a detailed CRM Analytics vs Tableau licensing and platform comparison — use architect/crm-analytics-vs-tableau-decision. NOT for building the app, lenses and datasets — use admin/crm-analytics-app-creation.
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Use when creating or configuring a CRM Analytics (Einstein Analytics) app — including app containers, lenses, datasets, data source connections, app sharing, and the row-level security that gates whether users see any data. NOT for choosing Reports vs CRM Analytics vs Tableau — use admin/einstein-analytics-basics. NOT for security predicate syntax, sharing inheritance or Analytics license assignment — use admin/analytics-permission-and-sharing.
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Use when creating or managing CRM Analytics datasets: configuring dataflows, scheduling refreshes, selecting fields, handling date types, managing row-count growth, or troubleshooting stale or broken datasets. Triggers: 'dataset creation', 'dataflow schedule', 'dataflow quota', 'date field not filterable', 'dataset row limit', 'CRM Analytics data refresh', 'field type mismatch in dataset'. NOT for making an existing dataset smaller or faster — use data/analytics-dataset-optimization. NOT for choosing between Reports, CRM Analytics and Tableau in the first place — use admin/einstein-analytics-basics.
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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.
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.
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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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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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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.
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.
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HuggingFace hf CLI: search/download/upload models, datasets.
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Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
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Data product builder — Datasets, APIs, dashboards, reports, data marketplace listing
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Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.
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