matchms
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
Browse published AI skills maintained by lifangda. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
80Repository stars across indexed skills
2,081Repository forks
400Average repository stars per indexed skill
26Repository stars may repeat across skills from the same repository.
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, paral...
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
Astronomy toolkit. FITS I/O, celestial coordinate transforms, cosmology calculations, time systems, WCS, units, astronomical tables, for astronomical data analysis and imaging.
Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel path...
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Manipulate AnnData objects for single-cell genomics. Load/save .h5ad files, manage obs/var metadata, layers, embeddings (PCA/UMAP), concatenate datasets, for scRNA-seq workflows.
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.