1.4만건 · 관련도순
Manage Fluid in KubeSphere with configuration files and commands for installation, data connections, caching, status checks, and troubleshooting. Prepare Dataset and runtime resources, preload data into cache, and adjust runtime replica counts.
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Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.
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Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.
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Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats. Triggers on: "create original data", "proprietary data", "survey design", "benchmark study", "original research", "data-driven content", "create a survey", "industry benchmark", "aggregated data", "unique data", "first-party data", "data moat", "generate research data", "create a study", "original statistics", "data nobody else has", "competitive data advantage".
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Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit
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Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-compliant data handling. Use when: "test data," "fixtures," "factories," "seed data," "synthetic data," "test database," "data anonymization." Not for: migration/integrity testing of the DB itself — use database-testing; environment provisioning and database branching strategy — use test-environments. Related: test-environments, database-testing, api-testing, unit-testing.
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Complete guide for AutoFixture and xUnit integration. Use when you need to use AutoData or InlineAutoData to simplify xUnit parameterized test data preparation. Covers custom Customization and test data attributes to improve test readability and maintainability. Keywords: AutoData, InlineAutoData, AutoFixture xUnit, [AutoData], [InlineAutoData], AutoDataAttribute, ICustomization, DataAttribute, parameterized test, Theory AutoData, MemberAutoData, test data attributes, fixture.Customize
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Automate Dadata Ru tasks through Rube MCP, a connection between an AI agent and external tools. The skill finds suitable operations, verifies the service connection, and runs the requested work with current input requirements.
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Automate Asin Data API tasks through Rube MCP, a connection between an AI agent and external tools. The skill discovers available operations, verifies the service connection, and runs tasks using the required input format.
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Find and download public cancer imaging data from NCI Imaging Data Commons. Select CT, MR, PET, or pathology datasets using metadata, view images in a browser, and check licenses and citations before using the data.
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Create or improve a company-specific data analysis skill by gathering analysts’ knowledge and examining database structures. Document business terms, metric formulas, table relationships, and common query patterns in reusable reference files.
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Use live DataForSEO data to research search results, keyword demand, backlinks, and competitors. Compare findings in tables, inspect page issues, and track how brands or websites are mentioned and cited in AI responses.
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Automate Dadata Ru tasks through Rube MCP, a connection between an AI agent and external tools. The skill finds suitable operations, verifies the service connection, and runs the requested work with current input requirements.
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Automate Asin Data API tasks through Rube MCP, a connection between an AI agent and external tools. The skill discovers available operations, verifies the service connection, and runs tasks using the required input format.
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Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.
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Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
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World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
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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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Automate Brightdata operations through Rube MCP and Composio. The skill discovers tools for a requested task, verifies the connection, and follows current input requirements to execute supported operations, including grouped workflows.
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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.
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Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.
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Review personal data handling against GDPR requirements, covering processing records, legal grounds, individual rights, breach procedures, and international transfers. Produce findings by article and a prioritized improvement roadmap with responsibilities, estimated costs, and timelines.
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Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.
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Prepare a local development environment for the Data Manager API with authentication and library installation guidance. It covers five programming languages and companion utilities for formatting, hashing, and encrypting user identifiers before ingestion.
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