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Found 285 Skills
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
Pandas for time series analysis, OrcaFlex results processing, and marine engineering data workflows
Build high-quality visual Web artifacts using HTML/CSS/JavaScript/React — web pages, landing pages, dashboards, interactive prototypes, HTML slide decks, animated demos, UI mockups, data visualizations, and more. Use this skill whenever the user's request involves a visual, interactive, or front-end deliverable, including: - Creating web pages, landing pages, dashboards, marketing pages - Building interactive prototypes or UI mockups (with device frames) - Building HTML slide decks / presentations - Creating CSS/JS animations or timeline-driven animated demos - Turning design mockups, screenshots, or PRDs into interactive implementations - Data visualization (Chart.js / D3, etc.) - Design system / UI Kit exploration Even if the user doesn't explicitly say "HTML" or "web page," this skill applies whenever the intent is to produce something visual, interactive, or presentational. Not applicable: pure back-end logic, CLI tools, data-processing scripts, non-visual code tasks, command-line debugging.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
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.
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.
Create, edit, and manipulate Excel spreadsheets programmatically using openpyxl
Work with raster and imagery data including ImageryLayer, ImageryTileLayer, multidimensional data, pixel filtering, and raster analysis. Use for satellite imagery, elevation data, and scientific raster datasets.
Write and query high-cardinality event data at scale with SQL. Load when tracking user events, billing metrics, per-tenant analytics, A/B testing, API usage, or custom telemetry. Use writeDataPoint for non-blocking writes and SQL API for aggregations.
Extract actionable Linear tickets from ambiguous input — Slack conversations, call transcripts, screenshots, meeting notes, or any unstructured material. Proposes tickets in a scratchpad file for user review, then creates them in Linear on approval. Use when the user wants to turn conversations, transcripts, screenshots, or notes into Linear tickets. Also use when user says "create tickets from this", "send to linear", "make issues from this call/chat", or provides raw material and asks for tickets.