nano-banana-pro
Generate/edit images with Nano Banana Pro (Gemini 3 Pro Image). Use for image create/modify requests incl. edits. Supports text-to-image + image-to-image; 1K/2K/4K; use --input-image.
Generate/edit images with Nano Banana Pro (Gemini 3 Pro Image). Use for image create/modify requests incl. edits. Supports text-to-image + image-to-image; 1K/2K/4K; use --input-image.
Analyze one or multiple images with Gemini Flash (vision) via the generateContent REST API. Uses Bun + TypeScript to base64-encode images and print model output. Use for captioning, OCR-like extraction, UI/screenshot analysis, and multi-image comparison.
Statistics, probability, linear algebra, and mathematical foundations for data science
Create, manipulate, and analyze geometries using geometry classes and geometry operators. Use for spatial calculations, geometry creation, buffering, intersections, unions, and mesh operations.
Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
Compute rolling 6h/1D correlations and betas vs BTC from local OHLCV CSVs, plus 4H CoC and 1H VWAP status. Use when the user requests rotation eligibility or structure checks.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Create advanced cartographic symbols using CIM (Cartographic Information Model). Use for complex multi-layer symbols, animated markers, custom line patterns, and data-driven symbology.
Parse and analyze personal financial transaction CSV exports to calculate account totals and generate detailed breakdowns. Use when the user asks to analyze transaction data, generate financial summaries, calculate account balances, or review spending from CSV exports. Supports account grouping (Galicia, Mercado Pago, Quiena, LLC/Relay, HSBC, Crypto), automatic internal transfer detection, and detailed transaction listings.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
This skill should be used when the user asks to "work with polars", "create a dataframe", "use lazy evaluation", "migrate from pandas", "optimize data pipelines", "read parquet files", "group by operations", or needs guidance on Polars DataFrame operations, expression API, performance optimization, or data transformation workflows.
Shipping Data Analysis - Brock's Reusable Prompt v4.0 Analyzes any shipping dataset with FirstMile-branded deliverables, normalized address grouping, and conditional advanced analytics based on available data fields. Use when: (1) Prospect provides shipping data (PLD, exports, etc.) (2) "analyze shipping data" or "shipping analysis" (3) "run Brock's prompt" or "data analysis v4.0" (4) Need DIM exposure, zone distribution, or cost intelligence reports (5) Creating prospect-facing shipping profile analysis Triggers on: "shipping analysis", "analyze shipping", "PLD analysis", "Brock's prompt", "data analysis v4.0", "shipping profile", "DIM exposure", "zone distribution"
Transform data sources to Nixtla schema (unique_id, ds, y) with column inference. Use when preparing data for forecasting. Trigger with 'map to Nixtla schema' or 'transform data'.
Master data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib
Work with temporal data using TimeSlider, TimeExtent, and time-aware layers. Use for animating data over time, filtering by date ranges, and visualizing temporal patterns.
Common patterns for extracting and combining analytics data from GA4, GSC, and SE Ranking. Includes API patterns, rate limiting, caching, and error handling.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.