Extract a text's core argument into an interconnected Q-A chain in org-mode format.
npx skills add https://github.com/lijigang/ljg-skills --skill ljg-qa
Please help me install this Agent Skill. Skill: ljg-qa Source: https://github.com/lijigang/ljg-skills/tree/main/skills/ljg-qa Command: npx skills add https://github.com/lijigang/ljg-skills --skill ljg-qa
读一本文章/论文/书籍时，将其论证骨架拆解为一条清晰的 Q-A 链，强调“为什么/怎么/边界”，以便复现作者推理而非简单摘要。
将文章或文本输入，生成一个5-10条Q-A链，输出为org-mode格式。
High-intent search queries and answers about installing and using this skill.
This Skill extracts the core argument by generating a 5-10 question sequence in org-mode format. It enforces a four-part answer structure covering conclusion, formalization, derivation, and boundaries to reproduce the author's reasoning.
Formalizing critical reading notes is best achieved by enforcing a four-part answer structure: Conclusion, Formalization, How it was derived, and Boundaries. This structure ensures extracted arguments are auditable and reproducible rather than simple summaries.
You generate org-mode formatted questions by inputting article URLs, PDFs, or text into the workflow. The Skill then outputs a directional Q-A chain that enforces logical flow and relevance between the interconnected questions.
Yes, this argument analysis workflow applies to articles, academic papers, and full books. It processes any input text to produce a 5-10 question sequence that reconstructs the original reasoning path for research or teaching.
A limitation of automated question design is that the output is restricted to a 5-10 question sequence in org-mode format. It focuses strictly on reasoning boundaries and derivation rather than producing comprehensive surface-level summaries.
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