physical-sciences eklentisi

physical-sciences içinde ne var

Eklentinin kendi dosyalarından okunan 10 parça.

physical-sciences içindeki beceriler

1. 
   cirqKalarisLabs/research-agent-skills

   Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

   7 dosya
   126yıldız
2. 
   fluidsimKalarisLabs/research-agent-skills

   Plan, configure, inspect, restart, and analyze bounded FluidSim computational-fluid-dynamics simulations with explicit numerical-validity and HPC safety checks. Use for FluidSim solver selection, parameter review, FFT/MPI setup, output diagnostics, or restart compatibility.

   16 dosya
   126yıldız
3. 
   geopandasKalarisLabs/research-agent-skills

   Guidance and local audit CLIs for Python workflows using GeoPandas 1.1.4 GeoSeries and GeoDataFrame for planar vector data: CRS handling, geometry validity and repair, sjoin, overlay, clip, dissolve, union, GeoPackage, GeoParquet and PostGIS I/O, and plotting. Use when reprojecting or assigning a CRS, auditing invalid or empty geometries, running spatial joins and checking cardinality, exporting vector data with a reproducible contract, or migrating code from GeoPandas 0.14 to 1.x. Use when checking privacy risks in coordinate data. Do not use for raster data or geodesic-only calculations.

   14 dosya
   126yıldız
4. 
   astropyKalarisLabs/research-agent-skills

   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.

   8 dosya
   126yıldız
5. 
   geomasterKalarisLabs/research-agent-skills

   Provides geospatial and Earth observation workflows using GeoPandas, Rasterio, GDAL, Xarray, Shapely, Laspy, PDAL, Google Earth Engine, and STAC/Planetary Computer, covering Sentinel, Landsat, MODIS, SAR and hyperspectral imagery, spectral indices, terrain and network analysis, point clouds, COGs, CRS handling, and spatial ML, with code in Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use when computing NDVI or other indices from satellite imagery, when running vector overlays, reprojection, or spatial statistics on shapefiles, GeoJSON, or GeoPackage data, when searching STAC catalogs and reading cloud-optimized GeoTIFFs, when classifying land cover or training ML models on Earth observation data, or when doing terrain, hydrology, or point cloud analysis. Not for general non-spatial data analysis or tabular ML.

   16 dosya
   126yıldız