data

Plugin data from marketplace knowledge-work-plugins in 8GG-Git/knowledge-work-plugins. Commands read from the repo's manifest — 1 month ago.

Skills (7)

• data-context-extractor

  Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers sc

• data-exploration

  Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.

• data-validation

  QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.

• data-visualization

  Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

• interactive-dashboard-builder

  Build self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling. Use when creating dashboards, building interactive reports, or generating shareable HTML files with charts and filters that work without a server.

• sql-queries

  Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

• statistical-analysis

  Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Slash commands (6)

• /data:analyze

  Answer data questions -- from quick lookups to full analyses

• /data:build-dashboard

  Build an interactive HTML dashboard with charts, filters, and tables

• /data:create-viz

  Create publication-quality visualizations with Python

• /data:explore-data

  Profile and explore a dataset to understand its shape, quality, and patterns

• /data:validate

  QA an analysis before sharing -- methodology, accuracy, and bias checks

• /data:write-query

  Write optimized SQL for your dialect with best practices

MCP servers (6)

• snowflake
• databricks
• bigquery

  connects to: https://bigquery.googleapis.com/mcp

• hex

  connects to: https://app.hex.tech/mcp

• amplitude

  connects to: https://mcp.amplitude.com/mcp

• atlassian

  connects to: https://mcp.atlassian.com/v1/mcp