> browse/registry

> xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my

> Gong Automation

Automate Gong conversation intelligence -- retrieve call recordings, transcripts, detailed analytics, speaker stats, and workspace data -- using natural language through the Composio MCP integration.

> googleads-automation

Automate Google Ads analytics tasks via Rube MCP (Composio): list Google Ads links, run GA4 reports, check compatibility, list properties and accounts. Always search tools first for current schemas.

> Customer.io Automation

Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Composio

> dask

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

> polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

> geopandas

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 dat

> pytorch-lightning

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

> seaborn

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

> prefect

Prefect is a modern workflow orchestration framework for Python data pipelines. Learn to define flows and tasks with decorators, handle retries and scheduling, create deployments, and monitor via the Prefect UI.

> tinybird

Build real-time analytics APIs with Tinybird — ingest millions of events and query with SQL over HTTP. Use when someone asks to "build analytics API", "Tinybird", "real-time analytics", "event analytics", "ClickHouse as a service", "usage metering", or "product analytics backend". Covers data ingestion, SQL pipes, API endpoints, and real-time dashboards.

> vector

Expert guidance for Vector, the high-performance observability data pipeline built in Rust by Datadog. Helps developers collect, transform, and route logs, metrics, and traces from any source to any destination with minimal resource usage. Vector replaces Logstash, Fluentd, and Filebeat with a single, faster tool.

> web-scraper

Extract structured data from web pages and load it into databases. Use when a user asks to scrape a website, build a data pipeline, extract data from a webpage, pull prices from a site, collect links, gather product listings, download page content, parse HTML, set up ETL, or automate data collection. Handles static HTML, JavaScript-rendered pages, anti-bot proxies (Bright Data), data transformation, deduplication, and database loading.

> duckdb

DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and analytical workloads where spinning up a server is overkill.

> airbyte

You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.

> dlt

You are an expert in dlt, the open-source Python library for building data pipelines. You help developers load data from any API, file, or database into warehouses and lakes using simple Python decorators — with automatic schema inference, incremental loading, and built-in data contracts. dlt is the "requests library for data pipelines."

> dbt

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

> great-expectations

Great Expectations is a Python framework for data quality testing and validation. Learn to define expectations, create validation suites, build data docs, and integrate with data pipelines for automated quality checks.

> motherduck

Expert guidance for MotherDuck, the serverless analytics platform built on DuckDB that combines local and cloud query execution. Helps developers run SQL analytics on cloud-hosted data, share datasets, and build hybrid local-cloud data pipelines using DuckDB's familiar interface.

> dagster

Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.