> 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.

> Customer.io Automation

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

> 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.

> 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

> 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.

> 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.

> 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.

> private-equity-investment-dd

把 BP/CIM、访谈纪要、数据室和财务法律材料转成证据驱动的私募股权尽调与 IC 决策包。Use for PE/VC, growth equity, buyout, M&A, strategic investment, Pre-IPO, or non-listed-company diligence, including evidence matrices, red flags, QoE, valuation/return cases, terms, management interviews, data requests, IC memos, and post-investment value creation.

> 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.

> 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.

> apache-spark

Process large-scale data with Apache Spark. Use when a user asks to process big data, run distributed computations, build ETL pipelines, perform data analysis at scale, or use PySpark for data engineering.

> data-validator

Validate data quality in CSV, JSON, and database exports by checking for missing values, type mismatches, duplicates, outliers, and schema violations. Use when building ETL pipelines, auditing data imports, checking data freshness, or ensuring data contracts between teams. Trigger words: data quality, validation, null values, duplicates, schema check, data contract, ETL, pipeline, data drift.

> 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.

> data-migration

When the user needs to migrate data between databases, transform schemas, or consolidate data sources. Use when the user mentions "data migration," "database migration," "migrate from MySQL to PostgreSQL," "schema migration," "ETL pipeline," "data transfer," "database consolidation," "legacy migration," or "move data between databases." Covers schema analysis, mapping, transformation, batch processing, validation, and cutover planning. For query optimization during migration, see sql-optimizer.

> 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.