AI Summarymq is a jq-like CLI tool for querying and transforming Markdown files, with development guidelines covering Rust conventions, project structure, and multi-platform integrations (LSP, DAP, WASM, Python). Developers building Markdown processing pipelines or contributing to the mq project benefit from these structured instructions.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "mq — Copilot Instructions" prompt rules to my project. Repository: https://github.com/harehare/mq Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
A jq-like Markdown query language for command-line processing
Project Overview
mq is a jq-like command-line tool for Markdown processing. Written in Rust, it allows you to easily slice, filter, map, and transform Markdown files.
Rust Code Conventions
• Always format and validate code using cargo fmt and cargo clippy • Add appropriate documentation comments to all public functions, structs, traits, enums, etc. • Use the miette crate for error handling and provide user-friendly error messages • Avoid panics whenever possible and return appropriate Result types • Write comprehensive tests and update related tests when adding or changing functionality
Commit Message Conventions
Use the following format for commit messages: ` <type>(<scope>): <description> [optional body] [optional footer] ` • Types include: • ✨ feat: New feature • 🐛 fix: Bug fix • 📝 docs: Documentation changes • 💄 style: Code style changes that don't affect behavior • ♻️ refactor: Refactoring • ⚡ perf: Performance improvements • ✅ test: Adding or modifying tests • 📦 build: Changes to build system or external dependencies • 👷 ci: Changes to CI configuration files and scripts • Write clear, concise, and descriptive commit messages. • Reference related issues or pull requests when relevant.
Documentation Guidelines
When adding new features, update the documentation. • Keep documentation up-to-date with code changes. • Use clear, concise language and provide usage examples. • Document all public APIs, commands, and features. • Update /docs and crate-level README.md files for new features or changes. • Add changelog entries for all user-facing changes. • Ensure documentation is consistent across all files and crates. • Use Markdown best practices for formatting and structure.
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Works With
Any AI assistant that accepts custom rules or system prompts