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Humanizer Chinese Text Editing

op7418/Humanizer-zh

Edits Chinese articles, comments, and documents to remove clichés, repetitions, and templated expressions, making text more natural while preserving facts, certainty, and author voice. Suitable for polishing existing text, not for authorship detection or guaranteeing passage through AI detectors.

LangChain LCEL Workflow Composition

langchain-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Master the art of building robust, production-grade LLM chains using LangChain Expression Language (LCEL). This guide covers advanced composition patterns, including sequential, parallel, and conditional branching chains. It also details prompt templating, structured output parsing (JSON/Zod), and context injection, enabling the creation of complex, multi-step AI pipelines.

LangChain LCEL Quick Start Guide

langchain-hello-world

jeremylongshore/claude-code-plugins-plus-skills

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

N8n Expression Syntax Guide

n8n-expression-syntax

czlonkowski/n8n-skills

Practical reference to validate and correct n8n expressions, covering {{ }} usage, $json/$node/$now/$env variables, webhook payload access, and common pitfalls so workflow authors write error-free automations.

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.

Polars Fast DataFrame

K-Dense-AI/claude-scientific-skills

Polars is a blazing fast in-memory DataFrame library based on Apache Arrow that replaces pandas for 1-100GB workloads, offering expression-based lazy execution, parallel operators, and rich I/O for ETL pipelines.

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.

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