Nushell is a modern, cross-platform shell that treats data as structured (tables, records) rather than raw text streams. It features a pipeline-based architecture where commands process structured data and can be composed using the pipe operator. The shell is built as a modular Rust workspace with distinct crates for the core engine, parsing, command implementation, plugin system, and CLI interface. Nushell emphasizes extensibility through a plugin system where plugins run as separate processes communicating via JSON-RPC, providing isolation and language flexibility. Key features include built-in support for various data formats (JSON, CSV, Excel, Parquet), SQL-like querying capabilities, interactive exploration, and cross-platform compatibility with Windows, macOS, and Linux.
This repository is a Next.js SEO library that provides React components for generating JSON-LD structured data (Schema.org) to enhance search engine optimization. The library offers over 50 specialized components for various Schema.org types (Article, Recipe, Product, Event, etc.) and a core JsonLdScript component for rendering JSON-LD tags. It includes utility processors for normalizing flexible input data into Schema.org-compliant formats and emphasizes security through XSS prevention in JSON-LD stringification. The architecture follows a modular component pattern with strong TypeScript typing, comprehensive testing (unit and E2E), and support for both Pages Router and App Router in Next.js. The library is actively maintained with recent updates, a changeset-based versioning system, and extensive documentation.
LangExtract is a Python library for extracting structured information (entities, relationships) from unstructured text using large language models (LLMs). It provides a production-grade pipeline: chunking long documents, building prompts with few-shot examples, invoking LLMs (Gemini, OpenAI, Ollama, or custom providers), parsing and fuzzy-aligning the output to source text, and generating interactive HTML visualizations. The architecture is modular with a plugin system for providers, a schema system for output constraints, and a resolver for alignment. It includes comprehensive tests, benchmarks, and CI/CD. The library is actively maintained with a clear deprecation path for v2.0.0. Notable strengths are the provider plugin system, fuzzy alignment algorithms, and format handling. Weaknesses include some deprecation debt and complex alignment code.
This repository is a curated collection of traditional Chinese patterns organized as structured open data. It contains 100 high-resolution pattern images accompanied by detailed metadata (in JSON format) and Chinese-language description pages. The data is organized into six thematic categories (Flora, Fauna & Beasts, Geometric, Cloud-Water-Stone, Auspicious Objects, Characters & Composite) and aggregated into machine-readable JSON and CSV formats. Each pattern includes standardized metadata fields for identification, categorization, semantic description, and visual characteristics, along with cryptographic hashes for verification. The project serves as a cultural heritage resource for designers, researchers, and educators interested in traditional Chinese decorative arts.
This is a document redaction and anonymization platform designed for processing unstructured data (documents, images, PDFs) and structured data (CSV, databases) to detect and redact sensitive information like PII, PHI, and financial data. The system features a React/TypeScript frontend with a Python/FastAPI backend, utilizing AI models for NER (HaS Text), OCR (PaddleOCR-VL), and visual detection (LocateAnything). It provides both single-file processing via a playground interface and batch processing workflows with review/approval steps. The platform includes role-based access control, audit logging, configurable presets for different industries, and structured data handling capabilities. Deployment is containerized with Docker and supports GPU acceleration for ML models.
This repository is a Python library and command-line tool for extracting structured data from HTML documents. It supports multiple formats including W3C HTML Microdata, JSON-LD, Microformat, Open Graph, RDFa, and Dublin Core. The project provides a main extract function that runs all enabled extractors and returns the results in a dictionary, with an option to convert the output to a uniform JSON-LD-like structure for easier consumption. The architecture is modular, with each format handled by a separate extractor class. The main orchestration function in `_extruct.py` manages the extraction process, error handling, and result aggregation. The command-line interface allows users to extract data from a URL or file and output JSON directly. The project includes an extensive test suite with sample HTML files and expected outputs for each format, covering valid cases, edge cases, and invalid inputs. Key reusable components include the uniform output transformation module (`extruct/uniform.py`), which normalizes data from different formats into a common structure, and the error handling pattern used throughout the extraction pipeline. The project is actively maintained, with recent updates adding support for newer Python versions and maintaining compatibility with dependencies. Technical drift is low due to regular updates and a focus on compatibility.
This repository is a Claude Code skill bundle for Generative Engine Optimization (GEO) and SEO auditing, distributed as an installable plugin. The core deliverable is a composite GEO Score (0-100) computed from six weighted categories: AI citability, brand authority, content E-E-A-T, technical health, structured data, and platform optimization. Users invoke slash commands such as /geo audit, /geo quick, /geo citability, /geo llmstxt, and /geo report, which orchestrate a set of skill definitions, agent prompts, and Python helper scripts to crawl external websites, analyze AI crawler accessibility, extract JSON-LD, generate llms.txt files, score content passages, and produce client-ready reports and proposals. It also includes a small Flask + HTMX CRM webapp for managing prospects and a white-label configuration system for agencies. The architecture is primarily a composition of markdown-based skills and agents with Python scripts as deterministic helpers. The main entry points are geo/SKILL.md (orchestrator), the scripts under scripts/, and the webapp at scripts/webapp/app.py. The system behaves as a productized agency service: it runs audits against arbitrary client domains, produces quantitative scores, generates branded PDF reports, pricing proposals, and monthly delta comparisons. Some research-oriented artifacts exist - cited market studies, scoring methodology documentation, and heuristic rubrics - but the dominant character is an application toolkit for practitioners rather than a data pipeline. Strengths include a clear JSON-to-stdout contract between scripts and skills, deterministic scoring modules such as citability_scorer.py and llmstxt_generator.py, reusable JSON-LD schema templates, and a sensible separation between deterministic checks and LLM-judged scoring. Weaknesses include documentation drift (inconsistent skill counts and command references), duplicated scoring rubrics in agent vs skill versions, a legacy ReportLab PDF pipeline documented alongside the newer pandoc/Chrome approach, and only narrow test coverage focused on fetch_page.py.