Extract logic-driven Q&A chains from unstructured sources into parallel Chinese and English Lark documents.
npx skills add https://github.com/RockerMJ031/mj-claude-skills --skill mj-qa
Please help me install this Agent Skill. Skill: mj-qa Source: https://github.com/RockerMJ031/mj-claude-skills/tree/main/mj-qa Command: npx skills add https://github.com/RockerMJ031/mj-claude-skills --skill mj-qa
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.
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.
High-intent search queries and answers about installing and using this skill.
To extract Q&A chains from unstructured research papers, you can automate the generation of structured question-answer pairs that follow the author's causality, tradeoffs, and failure boundaries to reconstruct the full reasoning path.
Generating bilingual Q&A pairs from industry reports is best handled by creating parallel Chinese and English versions with aligned logical structures, allowing cross-lingual teams to share reconstructed knowledge without reading the original source.
Logic-driven Q&A extraction differs from standard summarization by generating questions focused on causality and failure boundaries, with answers structured to include conclusions and reasoning paths rather than simply reformatting text into FAQs.
Yes, you can save extracted Q&A chains directly to a Lark wiki workspace by writing the Chinese content to a designated Lark folder and the English content to a synchronized Q&A Library folder.
Yes, Q&A chain extraction works for training material deconstruction and policy documents by applying strict formatting rules to question types and answer structures, ensuring the chain captures the full intellectual scaffolding of any unstructured source.
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