lifangda
- 39 skills
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- 1 week ago last updated
- ▌ Reportlab · lifangda bundlePDF generation toolkit. Create invoices, reports, certificates, forms, charts, tables, barcodes, QR codes, Canvas/Platypus APIs, for professional document automation.
- ▌ Scikit Learn · lifangda bundleML toolkit. Classification, regression, clustering, PCA, preprocessing, pipelines, GridSearch, cross-validation, RandomForest, SVM, for general machine learning workflows.
- ▌ Transformers · lifangda bundleWork with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
- ▌ Cellxgene Census · lifangda bundleQuery CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.
- ▌ Torch Geometric · lifangda bundleGraph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
- ▌ Datacommons Client · lifangda bundleWork with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
- ▌ Reactome Database · lifangda bundleQuery Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
- ▌ Async Python Patterns · lifangda bundleMaster Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
- ▌ Pyopenms · lifangda bundleMass spectrometry toolkit (OpenMS Python). Process mzML/mzXML, peak picking, feature detection, peptide ID, proteomics/metabolomics workflows, for LC-MS/MS analysis.
- ▌ Pysam · lifangda bundleGenomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
- ▌ Omero Integration · lifangda bundleMicroscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
- ▌ Microservices Patterns · lifangda bundleDesign microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
- ▌ Aeon · lifangda bundleTime series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices.
- ▌ Shap · lifangda bundleModel interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
- ▌ Pathml · lifangda bundleComputational 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 (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
- ▌ Get Available Resources · lifangda bundleThis skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
- ▌ Anndata · lifangda bundleManipulate AnnData objects for single-cell genomics. Load/save .h5ad files, manage obs/var metadata, layers, embeddings (PCA/UMAP), concatenate datasets, for scRNA-seq workflows.
- ▌ Pymoo · lifangda bundleMulti-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
- ▌ Arboreto · lifangda bundleGene regulatory network inference with GRNBoost2/GENIE3 algorithms. Infer TF-target relationships from expression data, scalable with Dask, for scRNA-seq and GRN analysis.
- ▌ Pydeseq2 · lifangda bundleDifferential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
- ▌ Histolab · lifangda bundleDigital 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 pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
- ▌ Pyhealth · lifangda bundleComprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
- ▌ Biopython · lifangda bundlePrimary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
- ▌ Deepchem · lifangda bundleMolecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
- ▌ Benchling Integration · lifangda bundleBenchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
- ▌ Exploratory Data Analysis · lifangda bundleEDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
- ▌ Polars · lifangda bundleFast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.
- ▌ Astropy · lifangda bundleAstronomy toolkit. FITS I/O, celestial coordinate transforms, cosmology calculations, time systems, WCS, units, astronomical tables, for astronomical data analysis and imaging.
- ▌ Etetoolkit · lifangda bundlePhylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
- ▌ Pylabrobot · lifangda bundleLaboratory automation toolkit for controlling liquid handlers, plate readers, pumps, heater shakers, incubators, centrifuges, and analytical equipment. Use this skill when automating laboratory workflows, programming liquid handling robots (Hamilton STAR, Opentrons OT-2, Tecan EVO), integrating lab equipment, managing deck layouts and resources (plates, tips, containers), reading plates, or creating reproducible laboratory protocols. Applicable for both simulated protocols and physical hardware control.
- ▌ Scikit Bio · lifangda bundleBiological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
- ▌ Zarr Python · lifangda bundleChunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
- ▌ Modern Javascript Patterns · lifangda bundleMaster ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.
- ▌ Geo Database · lifangda bundleAccess NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
- ▌ Pdb Database · lifangda bundleAccess RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
- ▌ Uspto Database · lifangda bundleAccess USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
- ▌ Ensembl Database · lifangda bundleQuery Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
- ▌ Matchms · lifangda bundleMass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
- ▌ Alphafold Database · lifangda bundleAccess AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.