mj-qa

What problem does it solve?

Manually reconstructing an author's full logical reasoning from unstructured sources like articles, books, papers, or training documents is time-consuming and often fails to capture the core intellectual scaffolding of the content. This skill automates that process by extracting structured, logic-driven Q&A chains that let readers follow and rebuild the author's original thought process.

Core Features & Use Cases

• Logic-Driven Q&A Extraction: Generates Q&A chains that follow the author's reasoning path (not just reformatted FAQs), with questions focused on causality, differences, tradeoffs, and failure boundaries, and answers structured with conclusions, formalized relations, reasoning paths, and failure boundaries.
• Parallel Bilingual Output: Creates synchronized Chinese and English versions of the Q&A chain, writing Chinese content to the Lark "问答" folder and English content to the "Q&A Library" folder for cross-lingual teams.
• Use Case: If your team needs to quickly digest a 30-page industry policy document and share its core logic with both Chinese and English speaking stakeholders, use this skill to generate aligned Q&A chains that let any reader rebuild the full argument without reading the original source.

Quick Start

Provide any source (article, book, paper, document link, or topic) and ask the mj-qa skill to generate a parallel bilingual Q&A chain that reconstructs the source's core reasoning, saved directly to your Lark workspace.