claudecodex

cre8-data

Write SQL, explore datasets, and build data pipelines with AI assistance. Includes Clerk machine authentication for reaching auth-gated data sources.

0 on GitHubby Tyler Morrowv2.0.0updated 2 months agoProductivity
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Plugin cre8-data from marketplace tmorrow_ai in tmorrowdev/tmorrow_ai. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (9)

  • api-data-contracts

    Generate typed data contracts from an OpenAPI spec so a UI agent can build data-driven interfaces without Claude ever seeing the underlying data. Triggers: "data contract", "API schema for UI", "wire up the API", "generate types from OpenAPI", "connect UI to API", "API integration layer" Use when th

  • auth-gated-data-sources

    Authenticate an agent to a data source that requires credentials, using Clerk machine authentication — M2M tokens, API keys, or OAuth tokens. Triggers: "auth gated data", "agent can't access the API", "401 from the data source", "authenticate the agent", "Clerk M2M", "machine token", "service accoun

  • 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 Snowflake SQL. Also supports BigQuery, Databricks, PostgreSQL, and other dialects. 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)

  • /cre8-data:analyze

    Answer data questions -- from quick lookups to full analyses

  • /cre8-data:build-dashboard

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

  • /cre8-data:create-viz

    Create publication-quality visualizations with Python

  • /cre8-data:explore-data

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

  • /cre8-data:validate

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

  • /cre8-data:write-query

    Write optimized SQL for your dialect with best practices

MCP servers (4)

  • snowflake

    connects to: https://app.snowflake.com/mcp

  • amplitude

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

  • atlassian

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

  • clerk

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

View source on GitHub

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