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Found 307 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.
Use before writing or editing any Python code — scripts, functions, bug fixes, cron jobs, data-processing one-offs, or additions to an existing .py file — even if the request sounds quick, casual, or "just a simple script." Applies whether the user names a .py file, describes what the script should do, or asks you to add/fix a function in Python. Covers required standards: type hints, no global variables, logging instead of print, wrapping any I/O (HTTP, DB, subprocess, file) in try/except, commenting the why not the what, writing a real test, and where to place design docs. Does not apply to conceptual Python questions with no code to write (e.g. explaining language features, comparing list vs tuple, explaining what an existing traceback means when the user hasn't asked for a fix, package install help, or learning-resource recommendations), and does not apply to non-Python languages.
OpenTelemetry Transformation Language (OTTL) expert for writing and debugging telemetry transformations in the OpenTelemetry Collector. Use when authoring or reviewing `transform`, `filter`, `tail_sampling` processor configs or `routing` connector configs, debugging OTTL syntax or semantics, transforming traces, metrics, logs, or profiles, or converting data-processing requirements into OTTL statements.
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.
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.
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.
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.