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lifangda AI Skills

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Explorez les skills IA publiés de lifangda et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

80

Étoiles des dépôts des skills indexés

2,081

Forks du dépôt

400

Moyenne des étoiles par skill indexé

26

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

80 skills

Explorer

GitHub

matchms

lifangda

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

GitHub

datamol

lifangda

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, paral...

GitHub

pymatgen

lifangda

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

GitHub

astropy

lifangda

Astronomy toolkit. FITS I/O, celestial coordinate transforms, cosmology calculations, time systems, WCS, units, astronomical tables, for astronomical data analysis and imaging.

GitHub

histolab

lifangda

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...

GitHub

etetoolkit

lifangda

Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.

GitHub

anndata

lifangda

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.

GitHub

pathml

lifangda

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...

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.