360 Secure SkillHub
360 安全技能中心

get QA pairs from text or session

核心功能:从聊天会话、文档等任意文本中,提取显性/隐性Q&A对(整合多轮内容、过滤闲聊),以及用户直接/隐含的偏好、约束等选择条件。典型场景:生成测验、整理聊天决策、构建知识库、制作闪卡等。输入为各类自由文本,输出分两大规范板块:结构化Q&A列表、标注来源的选择偏好表格,支持导出为MD文件。

goog
goog
热度 45.03
最近更新 2026-05-11 17:41:50
分类:效率依赖:读写文件 / 内部调用大模型应用场景:摘要总结 / 笔记整理 / 测验

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add text2qa
Metadata
name
text2qa
description
Extract structured Q&A pairs and Selection Preferences from any text source — especially the current chat session or uploaded documents. Use this skill whenever the user asks to "extract Q&A", "generate questions and answers", "pull out questions from the chat", "create a quiz from this conversation", "identify preferences", "find selection criteria", or wants to summarize a session into a reusable knowledge base. Also trigger when users say things like "turn this chat into Q&A", "what did we decide?", "document my preferences from this conversation", or "make flashcards from this". Works on chat sessions, documents, articles, transcripts, or any freeform text.

text2qa Skill

Extract Q&A pairs and Selection Preferences from the current chat session or any provided text.


Output Format

Produce two clearly separated sections:

Section 1: Q&A Pairs

Identify every implicit or explicit question-answer exchange. Format as:

## Q&A Pairs

**Q1: <question>**
A: <answer>

**Q2: <question>**
A: <answer>
...

Rules:

  • Extract both explicit questions (user directly asked something) and implicit questions (Claude provided info that answers an unstated question).
  • Rephrase conversational exchanges into clean, standalone Q&A pairs.
  • If an answer spans multiple turns, synthesize into one coherent answer.
  • Skip small talk or meta-conversation (e.g., "thanks!", "sure!").

Section 2: Selection Preferences

Identify any preferences, constraints, choices, or criteria the user expressed or implied. Format as:

## Selection Preferences

| # | Preference / Constraint | Source (direct/inferred) |
|---|------------------------|--------------------------|
| 1 | <preference>           | direct / inferred        |
| 2 | <preference>           | direct / inferred        |
...

Preference types to look for:

  • Format preferences — "I want markdown", "keep it short", "use bullet points"
  • Content preferences — "focus on X", "skip Y", "I prefer Z style"
  • Tool/approach preferences — "don't use files", "use Python not JS"
  • Persona/tone preferences — "be concise", "explain like I'm a beginner"
  • Domain/topic constraints — "only for production use", "target audience is X"
  • Decisions made — explicit choices the user made during the session

Mark each as direct (user stated it outright) or inferred (implied by behavior/choices).


Workflow

  1. Scan the full conversation (or provided text).
  2. Identify all Q&A exchanges and stated/implied preferences.
  3. Output Section 1 (Q&A Pairs) then Section 2 (Selection Preferences).
  4. Optionally offer to export as a .md file if the user might want to save it.

Tips

  • For chat sessions: scan from the very first message.
  • For documents: treat headings/paragraphs as implicit questions when appropriate.
  • If the session is very long, cluster related Q&As under sub-headings by topic.
  • If no preferences are found, say so clearly rather than inventing them.
  • Always note the total count: "Found X Q&A pairs and Y preferences."