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Hybrid Parsing: Regex vs LLM Decision Framework

regex-vs-llm-structured-text

affaan-m/everything-claude-code

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

Cellxgene Census Explorer

cellxgene-census

K-Dense-AI/claude-scientific-skills

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

COSMIC Mutation Access

cosmic-database

K-Dense-AI/claude-scientific-skills

Programmatic access to the COSMIC cancer mutation database for downloading mutations, Cancer Gene Census, mutational signatures, fusions, copy number, expression, and resistance data to fuel oncology research or bioinformatics pipelines after authentication.

GEO Data Retriever

K-Dense-AI/claude-scientific-skills

Connect to NCBI's GEO repository, search series/samples/platforms via Entrez queries, fetch profiles, and download raw/processed matrices using GEOparse for transcriptomics and expression analysis workflows.

PyDESeq2 Differential Analysis

K-Dense-AI/claude-scientific-skills

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Reactome Pathway Explorer

reactome-database

K-Dense-AI/claude-scientific-skills

Query the Reactome Content and Analysis Services (or the reactome2py client) to fetch pathway hierarchies, map genes, run overrepresentation/expression analyses, and explore disease interactions for systems biology research.

Single-Cell Generative Toolkit

K-Dense-AI/claude-scientific-skills

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

cBioPortal Cancer Data

cbioportal-database

K-Dense-AI/claude-scientific-skills

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

GTEx Expression Explorer

K-Dense-AI/claude-scientific-skills

Query the GTEx portal and API to retrieve tissue-specific gene expression, eQTL, sQTL, and variant-level regulatory data for interpreting GWAS loci and exploring gene regulation across 54 human tissues.

Gene Regulatory Network Inference Tool

K-Dense-AI/scientific-agent-skills

Infers candidate gene regulatory networks from bulk or single-cell expression data using GRNBoost2 and GENIE3 algorithms. Ranks transcription factor-target associations with Dask-compatible parallel execution, sparse expression support, and network stability validation for biological research applications.

CELLxGENE Census Data Query

cellxgene-census

K-Dense-AI/scientific-agent-skills

Programmatically query the CZ CELLxGENE Census for versioned single-cell and spatial transcriptomics data, including population-scale cell metadata, gene expression slices, summary counts, source H5AD URIs, embeddings, and reference atlas comparisons.

Bioinformatics Database Query Tool

K-Dense-AI/scientific-agent-skills

gget is a command-line and Python package providing unified access to over 20 genomic databases and analysis methods. It supports querying gene information, sequence analysis, protein structures, viral sequences, expression data, and disease associations. Suitable for interactive exploration and simple queries, offering both CLI and Python function interfaces for bioinformatics workflows.

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