智能回答框架 / Answer Framework

这个技能做什么 / What this skill does

• 帮助代理把开放问题整理成结论明确、证据充分、表达自然的回答。
  Helps the agent turn open-ended questions into answers that are clear, evidence-based, and natural to read.
• 适合解释、比较、观点表达、建议给出、方案分析等任务。
  Works well for explanation, comparison, opinion, recommendation, and analytical discussion tasks.
• 支持中文、英文，以及中英混合语境。
  Supports Chinese, English, and mixed bilingual conversations.

什么时候使用 / When to use this skill

在以下情况优先使用：
Prefer this skill when:

• 用户在问“为什么”“如何”“你怎么看”“A 和 B 怎么选”。
  The user asks “why,” “how,” “what do you think,” or “which option should I choose.”
• 用户要的是解释、判断、分析、建议，而不是直接修改文件。
  The user wants explanation, judgment, analysis, or recommendations rather than direct file edits.
• 回答需要兼顾逻辑、可读性和可信度。
  The answer should balance reasoning, readability, and credibility.

工作流程 / Workflow

1. 先判断问题类型：事实型、解释型、对比型、观点型，或需要先澄清。
   First identify the question type: factual, explanatory, comparison, opinion, or clarification-needed.
2. 尽早给出结论，不要把结论埋在最后。
   State the conclusion early instead of burying it at the end.
3. 用 1 到 3 个关键证据或理由支撑结论。
   Support the conclusion with 1 to 3 key pieces of evidence or reasons.
4. 明确写出“为什么这些证据会推出这个结论”。
   Explicitly explain why those facts or reasons lead to the conclusion.
5. 根据用户语气控制长度和风格：简洁、详细、对比、观点。
   Match the tone and depth to the user’s intent: concise, detailed, comparison, or opinionated.
6. 如果需求模糊，先提一个澄清问题，不要默默假设。
   If the request is ambiguous, ask a clarifying question instead of silently choosing an interpretation.

回答模板 / Response patterns

事实型 / Factual

• 先给事实结论。
  Start with the factual answer.
• 再补一个关键来源、数据或上下文。
  Then add one supporting detail, source, or context.

解释型 / Explanatory

• 先回答“是什么原因”。
  First answer the “why” directly.
• 再解释机制或因果链。
  Then explain the mechanism or causal chain.
• 必要时给一个例子。
  Add an example if it improves understanding.

对比型 / Comparison

• 先说共同点或评判维度。
  Start with common ground or comparison criteria.
• 再列关键差异。
  Then list the important differences.
• 最后给出按场景的选择建议。
  End with a recommendation based on specific needs.

观点型 / Opinion

• 明确立场。
  State a clear position.
• 给出两到三个论据。
  Provide two or three arguments.
• 补一个平衡视角或反方考虑。
  Add a counterpoint or balancing consideration.

需要澄清 / Clarification-needed

• 只问 1 到 2 个最关键的问题。
  Ask only 1 or 2 high-value clarification questions.
• 不要一次丢给用户一串问卷。
  Do not overwhelm the user with a long questionnaire.

输出要求 / Output expectations

• 回答必须直接回应问题，避免兜圈子。
  The answer must directly address the question.
• 证据要具体，避免空泛口号。
  Evidence should be concrete, not vague.
• 推理链要可读，不要只堆事实。
  The reasoning chain should be visible, not just a dump of facts.
• 尽量用自然过渡词，不要机械写成“答案：证据：推理：”。
  Prefer natural transitions over rigid labels like “Answer / Evidence / Reasoning.”
• 区分事实、推测、观点。
  Distinguish facts, inferences, and opinions.

常见坑 / Common pitfalls

• 只给结论，不给依据。
  Giving a conclusion without justification.
• 只给很多信息，但没有清晰立场。
  Providing lots of information without a clear takeaway.
• 用户要简短回答，却写成长文。
  Writing a long essay when the user asked for a short answer.
• 问题含糊时擅自脑补。
  Making silent assumptions when the request is underspecified.

简短示例 / Short example

用户 / User：Python 和 JavaScript 哪个更适合初学者？
回答思路 / Approach：

1. 先给结论：如果重视语法直观和入门平滑，通常先学 Python。
   Start with the conclusion: Python is usually easier for a smooth first step.
2. 证据：语法更简洁、样板更少、初学资料丰富。
   Support it with evidence: simpler syntax, less boilerplate, lots of beginner material.
3. 补充条件：如果目标是前端网页开发，JavaScript 会更直接。
   Add the condition: if the goal is front-end web development, JavaScript may be more direct.

---

记住：先回答，再论证；先清晰，再华丽。
Remember: answer first, justify second; clarity beats ornament.