「data」の検索結果

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, row filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.

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[Deprecated] Optional advanced tool for complex data modeling. For simple MySQL table creation, use relational-database-tool directly; for PostgreSQL / CloudBase PG schema work, use postgresql-development. New environments should use PostgreSQL DDL via queryPgDatabase/managePgDatabase — see postgresql-development skill instead.

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Populates Dataverse tables with sample records for testing and demoing a Power Pages site. Use when the user wants to add sample data, seed data, generate test records, or insert demo data into their tables.

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Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.

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Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.

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Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation mapping.

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Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.

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Data-driven resource harvesting (mining, logging, foraging) for Godot 4: apply_hit tool/tier validation via HarvestToolData enums, HarvestResourceData yields, harvestable_node shake/deplete, respawn_manager world persistence, UNIX offline progress, autosave, and noise vein proc-gen. Trigger keywords: apply_hit, HarvestResourceData, HarvestToolData, offline UNIX, respawn_manager, tool tier, harvestable_node, FastNoiseLite veins.

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-driven resource harvesting (mining, logging, foraging) for Godot 4: apply_hit tool/tier validation via HarvestToolData enums, HarvestResourceData yields, harvestable_node shake/deplete, respawn_manager world persistence, UNIX offline progress, autosave, and noise vein proc-gen. Trigger keywords: apply_hit, HarvestResourceData, HarvestToolData, offline UNIX, respawn_manager, tool tier, harvestable_node, FastNoiseLite veins.

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Clean and normalize A-share financial data for modeling. Adapts the original clean-data-xls skill for Chinese financial statements, CAS conventions, and A-share data formats. Triggers on "A股数据清洗", "财务数据清洗", "clean financial data China", "数据清洗", or "normalize financial statements".

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An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

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The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to

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Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz

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In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio

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Expert knowledge for Azure Database Migration Service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when planning Azure DMS migrations for MySQL, PostgreSQL, MongoDB, SSIS-to-Azure SQL, or hybrid workloads, and other Azure Database Migration Service related development tasks. Not for Azure Migrate (use azure-migrate), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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Expert knowledge for Azure Data Explorer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing ADX (RBAC/MI), automating cluster/schema deploys, tuning ingestion/queries, or integrating via ODBC/JDBC/Power BI, and other Azure Data Explorer related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).

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Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when managing DSVM images/tools, IaC deployment (Bicep/ARM), Key Vault secrets, MLflow, or GPU/Jupyter issues, and other Azure Data Science Virtual Machines related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).

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Expert knowledge for Azure Data Box development including troubleshooting, best practices, limits & quotas, security, configuration, and integrations & coding patterns. Use when using Data Box/Disk/Heavy for offline data copy, SMB/NFS uploads, REST APIs, CMKs/Key Vault, or NTFS ACL preservation, and other Azure Data Box related development tasks. Not for Azure Import Export (use azure-import-export), Azure Stack Edge (use azure-stack-edge), Azure Virtual Machines (use azure-virtual-machines).

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Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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Generate branded infographic specifications from any content or data. Outputs structured layout, copy, data visualization, and color scheme — ready to render as HTML/CSS, Satori, Canva, or any design tool. Use this skill when the user wants an infographic, data visual, social media image, comparison chart, stat card, or says "create an infographic for [content]", "make a visual for my LinkedIn post", "design an image for [topic]", "stat graphic for [data]", "comparison infographic", "branded image", "social media graphic", "infographic for [blog post]", "data visualization", "visual content", "image for my post", "LinkedIn carousel image", "feature comparison chart", "pricing table image".

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Build secure, reusable data access patterns with DTOs, taint checks, and colocated authorization in Next.js. Use when centralizing database queries, transforming raw data to DTOs, adding server-only guards, or preventing sensitive data from reaching Client Components.

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Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

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Use when you need to add or review Flyway database migrations in a Micronaut application — micronaut-flyway, db/migration scripts, flyway.datasources.* configuration, and alignment with JDBC or Micronaut Data. This should trigger for requests such as Add or review Flyway migrations in a Micronaut project; Configure micronaut-flyway or db/migration layout; Add versioned Flyway SQL migrations for Micronaut; Review Micronaut database migration ordering; Configure Flyway datasources in a Micronaut project. Part of Plinth Toolkit

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。