每个推荐都保留与其仓库、审计和安装路径的明确关联。
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
An open-source JavaScript library for world-class 3D globes and maps :earth_americas:
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
:earth_americas: machine learning tutorials (mainly in Python3)
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.
:earth_africa: :clipboard: A web dashboard to inspect Terraform States
Run a 5-dimension expert design review on any HTML artifact in the project — Philosophy / Visual hierarchy / Detail / Functionality / Innovation, each scored 0–10. Outputs a single self-contained HTML report with a radar chart, evidence-backed scores, and three lists: Keep / Fix / Quick-wins. Use when the brief asks for a "design review", "design critique", "5 维度评审", "design audit", or "what's wrong with my design".
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Geospatial resources for web development :earth_africa: 🗺️
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
An open-source JavaScript library for world-class 3D globes and maps :earth_americas:
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
:earth_americas: machine learning tutorials (mainly in Python3)
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.
:earth_africa: :clipboard: A web dashboard to inspect Terraform States
Run a 5-dimension expert design review on any HTML artifact in the project — Philosophy / Visual hierarchy / Detail / Functionality / Innovation, each scored 0–10. Outputs a single self-contained HTML report with a radar chart, evidence-backed scores, and three lists: Keep / Fix / Quick-wins. Use when the brief asks for a "design review", "design critique", "5 维度评审", "design audit", or "what's wrong with my design".
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Geospatial resources for web development :earth_africa: 🗺️
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
An open-source JavaScript library for world-class 3D globes and maps :earth_americas:
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
:earth_americas: machine learning tutorials (mainly in Python3)
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.
:earth_africa: :clipboard: A web dashboard to inspect Terraform States
Run a 5-dimension expert design review on any HTML artifact in the project — Philosophy / Visual hierarchy / Detail / Functionality / Innovation, each scored 0–10. Outputs a single self-contained HTML report with a radar chart, evidence-backed scores, and three lists: Keep / Fix / Quick-wins. Use when the brief asks for a "design review", "design critique", "5 维度评审", "design audit", or "what's wrong with my design".
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Geospatial resources for web development :earth_africa: 🗺️
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
An open-source JavaScript library for world-class 3D globes and maps :earth_americas:
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
:earth_americas: machine learning tutorials (mainly in Python3)
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.
:earth_africa: :clipboard: A web dashboard to inspect Terraform States
Run a 5-dimension expert design review on any HTML artifact in the project — Philosophy / Visual hierarchy / Detail / Functionality / Innovation, each scored 0–10. Outputs a single self-contained HTML report with a radar chart, evidence-backed scores, and three lists: Keep / Fix / Quick-wins. Use when the brief asks for a "design review", "design critique", "5 维度评审", "design audit", or "what's wrong with my design".
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Geospatial resources for web development :earth_africa: 🗺️