data-metabase
Metabase REST API automation: auth, export/upsert cards and dashboards, visualization_settings. Use when scripting Metabase via API.
Metabase REST API automation: auth, export/upsert cards and dashboards, visualization_settings. Use when scripting Metabase via API.
Analyze datasets to extract insights through statistical methods, trend identification, hypothesis testing, and correlation analysis.
Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly.
Bulk reads, multi-page iteration, and analytics using the Python SDK and Web API. Use when: "query records", "read data", "bulk read", "all records", "iterate records", "expand lookup", "cross-table query", "join tables", "aggregate", "group by", "average", "sum", "count with HAVING", "$apply", "N:N expand", "notebook", "pandas", "DataFrame", "analyze data", "load into dataframe". Do not use when: MCP is sufficient (simple filter, single-record read, small result set — use MCP first), creating or modifying records (use dv-data), creating tables or columns (use dv-metadata).
Explore an Axiom dataset to understand its schema, fields, volume, and patterns. Use when discovering a new dataset, investigating data structure, or understanding what data is available.
Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling.
UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.
Consult Snowflake CREATE HYBRID TABLE parameter reference before generating any CREATE HYBRID TABLE DDL.
Consult Snowflake CREATE CORTEX SEARCH SERVICE parameter reference before generating any CREATE CORTEX SEARCH SERVICE DDL.
Master the AI tools that transform financial operations. From bookkeeping to forecasting, manage money smarter and make data-driven financial decisions. Use when "finance, accounting, budgeting, forecasting, FP&A, bookkeeping, expense management, finance, accounting, budgeting, forecasting, fpa, expenses" mentioned.
Expert in measuring what matters in communities. Covers health metrics, engagement analytics, sentiment analysis, cohort tracking, and reporting. Knows that good data drives good decisions, and bad metrics drive bad behavior. Use when "community metrics, community analytics, measure community, community health, engagement metrics, community reporting, " mentioned.
日本の行政オープンデータにアクセスする。不動産取引価格(国交省)、官公需・入札情報、e-Stat政府統計の3つのデータソースを検索・取得できる。「不動産価格を調べて」「入札情報を検索」「政府統計を取得」「官公需で○○を探して」「地価を調べて」「統計データ」などで使用。MCP server (mcp.n-3.ai) 経由でリアルタイムにデータ取得する。
Extract HEC-RAS hydraulic results from HDF files including water surface elevations (WSE), depths, velocities, and flows for both steady and unsteady simulations. Handles cross section time series, 2D mesh results, maximum envelopes, and dam breach results. Use when you need to extract, analyze, or post-process HEC-RAS simulation outputs, retrieve water levels, query velocity fields, get depth grids, extract flow data, analyze breach hydrographs, or pull hydraulic variables from .hdf result files. Triggers: HDF results, extract WSE, water surface elevation, depth grid, velocity, flow data, mesh results, cross section time series, maximum envelope, breach results, HdfResultsPlan, HdfResultsMesh, HdfResultsBreach, steady results, unsteady results, plan HDF, .p01.hdf, get_wse, get_depth, get_velocity, post-process, simulation output.
Reads HEC-DSS files (V6 and V7) for boundary condition extraction using RasDss class. Handles JVM configuration, HEC Monolith download, catalog reading, and time series extraction. Use when working with DSS files, extracting boundary data, reading HEC-HMS output, integrating DSS workflows with HEC-RAS, cataloging DSS file contents, or converting DSS data to pandas DataFrames. Triggers: DSS, HEC-DSS, boundary condition, time series, JVM, Java, catalog, pathname, HEC-HMS, Monolith, pyjnius, read DSS, extract DSS, DSS boundary, DSS catalog, DSS DataFrame, DSS V6, DSS V7.
Analyze spatial variability of NOAA Atlas 14 precipitation frequency estimates within HEC-RAS model domains using intelligent extent-based downloading. Helps determine whether uniform rainfall assumptions are appropriate for rain-on-grid modeling by calculating min/max/mean/range statistics within 2D flow areas or project extents. Uses NOAA CONUS NetCDF with HTTP byte-range requests for 99.9% data reduction compared to traditional state-level ZIP downloads. Use before rain-on-grid modeling to assess uniform rainfall validity, for large model domains where spatial variance matters, for multi-event comparison across return periods, or for engineering report generation. Triggers: atlas 14 variance, atlas14 variance, precipitation variance, spatial variance analysis, uniform rainfall, spatially variable rainfall, precipitation spatial variability, rain-on-grid variance, atlas 14 grid, atlas14 grid, noaa atlas 14 conus, precipitation frequency grid, assess uniform rainfall, extent-based precipitation, 2d flow
Complete USGS gauge data integration workflow from spatial discovery to model validation. Handles gauge finding, data retrieval, matching to HEC-RAS features, boundary condition generation, initial conditions, real-time monitoring, and validation metrics (NSE, KGE). Use when working with USGS data, NWIS gauges, generating boundaries from observed flow, calibrating models, validating with observed data, or setting up operational forecasting. Triggers: USGS, NWIS, gauge, streamflow, observed data, boundary condition from gauge, calibration, validation, NSE, KGE, spatial discovery, gauge matching, real-time monitoring, initial conditions, GaugeMatcher, UsgsGaugeSpatial, RasUsgsCore, flow data, stage data, discharge, rating curve, observed flow, gauge near model.
Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when "segment, analytics.js, customer data platform, cdp, tracking plan, event tracking, identify track page, data routing, segment, cdp, analytics, tracking, data-pipeline, customer-data" mentioned.
Comprehensive statistical analysis for research, experiments, and data science. Covers hypothesis testing, effect sizes, confidence intervals, Bayesian methods, regression, and advanced techniques. Emphasizes correct interpretation and avoiding common statistical mistakes. Use when ", " mentioned.
Tidy evaluation and programmatic tidyverse patterns using rlang. Use this skill when writing functions that accept column names as arguments, building tidyverse-compatible APIs, or working with data-masking and injection operators. Covers embracing with {{}}, injection (!! and !!!), dynamic dots, .data/.env pronouns, name injection with glue syntax, bridge patterns between selection and data-masking, and package development with rlang.