ghqr is a Go-based CLI tool for scanning GitHub enterprises, organizations, and repositories against a comprehensive set of best-practice rules. It produces actionable reports in Markdown, Excel, and JSON formats, with prioritized findings, remediation plans, and manual checklists. The tool also includes an MCP (Model Context Protocol) server for AI assistant integration, enabling interactive scanning and recommendations via stdio or HTTP/SSE. A deterministic mock generator allows demos and testing without live API calls. The architecture follows a composable pipeline pattern: initialization, data loading (from live API or JSON replay), enterprise/GHES/organization/repository discovery and scanning, evaluation against YAML-defined rules, and report rendering. Scanners use both REST and GraphQL APIs with batch queries, rate-limit handling, and tri-state boolean support for GHES on-prem instances. The rule registry is embedded as YAML files, loaded at startup into a typed registry that evaluators reference by stable rule IDs. Key entry points are the `ghqr scan`, `ghqr mcp`, `ghqr mock`, and `ghqr list-recommendations` commands. External dependencies include the GitHub API (REST and GraphQL), the `excelize` library for Excel output, and the `mcp-go` library for MCP protocol support. The tool is containerized via Docker and published to GHCR, with install scripts for Linux, macOS, and Windows.
This repository is a crowdsourced collection of quiz questions and answers for LinkedIn skill assessments across dozens of technical domains (programming languages, frameworks, cloud platforms, design tools, etc.). The data is organized into topic directories, each containing a markdown file with multiple-choice questions, correct answers marked, and occasional reference links. Translations into multiple languages are provided. The repository includes a Python script (translate.py) for automated translation using Google Translate. The primary purpose is as a study aid for job seekers and interview preparation. The content is static and not actively maintained, leading to potential inaccuracies for current software versions. It is not an application or data pipeline but rather a curated knowledge base.