Results for “data-table”

Create a structured quarterly business report with table of contents, data tables, and page numbers.

Explains MariaDB-specific TRUNCATE TABLE semantics: DROP privilege, implicit commit, AUTO_INCREMENT reset, trigger and FK behavior, and restrictions on system-versioned tables and sequences.

Explains MariaDB explicit table locking (LOCK TABLES/UNLOCK TABLES) and named user-level locks (GET_LOCK family), covering alias traps, implicit commits, and multi-lock semantics for writing or reviewing locking code.

Comprehensive exploratory data analysis with publication-quality descriptive tables, correlation matrices, distribution plots, and assumption testing. Generates a standalone EDA report with Table 1 (gtsummary/great_tables), correlation heatmap, distribution diagnostics, VIF for multicollinearity, and normality/homoscedasticity tests. All figures are APA-formatted and colorblind-safe. Use when the user says "exploratory analysis," "EDA," "descriptive statistics," "explore the data," "Table 1," "correlations," "distributions," or when /data-clean completes successfully. Triggers on "EDA," "descriptive," "Table 1," "explore," "correlations."

Use for writing, reviewing, debugging, or testing publication-quality display tables in Python with Great Tables, including GT construction, stub and row groups, headers and spanners, labels, numeric/date formatting, targeted styles, footnotes/source notes, HTML/LaTeX/image export, and render verification. Do not use for dataframe computation, interactive data grids, charts, or plain console tables.

Adds a new database table and generates all six layers of the platform codebase, from schema to UI, with validation at each step.

Documents MariaDB-specific DROP TABLE syntax and behavior, including RESTRICT/CASCADE no-ops, DROP TEMPORARY TABLE, partial drops, IF EXISTS warnings, foreign key references, views, triggers, implicit commit, and WAIT/NOWAIT. Use when writing, generating, or reviewing DROP TABLE statements targeting MariaDB.

Provides code samples and best practices for using the Azure Tables SDK for Python to perform NoSQL key-value storage, entity CRUD, batch operations, and queries against Azure Storage Tables or Cosmos DB Table API.

Query external data from Oxla — Kafka topics via catalogs, Apache Iceberg tables, and S3/GCS/Azure parquet/ORC files — alongside native Oxla tables. Use when querying Kafka topics with CREATE KAFKA CATALOG or CREATE REDPANDA CATALOG, reading Apache Iceberg tables with the catalog=>path.table syntax, loading or.

Generates a comprehensive, editable schema reference file for a data warehouse, including table metadata, row counts, and codebase context, to enable instant concept-to-table lookups.

Documents MariaDB-specific UPDATE syntax and behavior, including single-table vs multi-table forms, assignment evaluation order, RETURNING with OLD_VALUE(), temporal tables, and common pitfalls. Use when writing, generating, or reviewing UPDATE statements targeting MariaDB.

Guides Lightning Web Component developers in setting up lightning-datatable with stable row identity, typed columns, row actions, inline edit, and bounded infinite loading.

Converts CSV, Excel, or JSON data into a polished, interactive visual report page with KPI cards, charts, data tables, and insights.

Generate a Notion-style live team dashboard with KPI cards, 7-day sparklines, activity feed, and task table, using seed data when no data source is available.

Econometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"

Provision Bigtable instances, design performant schemas, and query data using gcloud, cbt, or client libraries.

Write and run analytical SQL queries against Redpanda SQL (Oxla), a distributed PostgreSQL-wire-compatible columnar database, including data loading, external sources, and type handling.

Structured data + RAG. NL2SQL hybrid patterns (text-to-SQL then execute vs embed rows), table embedding strategies (row-level, schema-level, hybrid), semantic layer integration (Cube, dbt metrics), LangChain SQLDatabaseChain, LlamaIndex PandasQueryEngine, safe SQL execution (read-only, sandboxed), schema-aware retrieval. Full PostgreSQL + pgvector hybrid code. USE WHEN: user mentions "tabular RAG", "NL2SQL", "text to SQL", "RAG on tables", "database RAG", "SQL RAG", "semantic layer", "structured data RAG" DO NOT USE FOR: unstructured doc RAG - use `rag-architecture`; metadata filtering only - use `self-querying-retriever`; KG retrieval - use `graph-rag`

Design and format publication-quality tables: column order, row grouping, notes, precision, reproducibility.

**Structure:**

Provides a dashboard template for community/dating metrics with a navigation rail, ticker bar, headline KPIs, 30-day mutual-matches bar chart, and match-rate trend block.

飞书多维表格（Base）：基于需求搭建系统和应用，或把已有数据、零散信息结构化，搭建成可持续记录、收集、整理、关联、协作、统计、提醒和流转的数据工具与管理系统。适用于个人、组织、企业的日常记录、清单登记、资料库、报名问卷、进度跟踪及项目/客户/订单/库存等系统和应用的生成需求；用户想把信息记下来、管起来、统计查看、持续更新或自动处理时使用，或提及 Base/多维表格/bitable、提供 Base 链接时使用；已有 Base 的查询、编辑和分析同样使用。

Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.

Answer any data question, scaling effort from a one-number lookup to a full stakeholder-ready analysis with a recommendation.

Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.

Automate Snowflake data warehouse operations: list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP integration.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

Time-Series Data

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.