Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alizeeblack-code/zhihu-mcp --skill analyze-zhihu-questiongit clone --depth 1 https://github.com/alizeeblack-code/zhihu-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-question)<a href="https://agentmods.dev/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-question"><img src="https://agentmods.dev/badge/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-question/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-question"><img src="https://agentmods.dev/badge/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-question.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.01031 |
| Opus 5 | $0.00028 | $0.00515 |
| Sonnet 5 | $0.00011 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
analyze-zhihu-question scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
知乎问题深度分析
对一个具体的知乎问题进行深度分析,阅读高赞回答全文和评论,输出观点总结。
适用场景
- 用户给出一个知乎问题链接,想要快速了解讨论全貌
- 用户说"帮我分析一下这个问题"、"总结一下这个问题的回答"
- 用户想知道某个问题下大家的主流看法
工作流程
用户输入问题 URL 或 ID
│
├─ Step 1: 解析输入,获取 question_id
│
├─ Step 2: 获取问题详情和回答列表
│
├─ Step 3: 逐个阅读高赞回答全文
│
├─ Step 4: 获取高争议回答的评论
│
└─ Step 5: 输出深度分析
Step 1: 解析输入
从用户输入中提取 question_id:
- URL 格式
https://www.zhihu.com/question/12345678→ question_id =12345678 - URL 含回答
https://www.zhihu.com/question/12345678/answer/87654321→ question_id =12345678 - 纯数字
12345678→ 直接使用
Step 2: 获取问题详情
get_question_detail(question_id="12345678")
从结果中获取:
- 问题标题和描述
- 话题标签
- 回答数、关注数、浏览数
- Top 回答列表(最多 10 个,含摘要和点赞数)
向用户简要汇报问题概况:
问题:{标题}
话题:{话题1}、{话题2}
{回答数} 个回答 · {关注数} 人关注 · {浏览数} 次浏览
正在阅读 Top {N} 高赞回答...
Step 3: 阅读高赞回答
根据回答列表,选取点赞数最高的 3-5 个回答,逐个获取全文:
get_answer_detail(question_id="12345678", answer_id="回答ID")
对每个回答,提取:
- 作者和作者简介
- 完整内容的核心论点
- 点赞数和评论数
如果回答内容很长,提取关键段落和结论,不需要逐字复述。
Step 4: 获取评论(选择性)
对以下回答获取评论:
- 评论数 > 50 的高争议回答
- 用户特别指定的回答
- 观点最对立的回答
get_comments(url="回答URL", count=15)
关注评论中的:
- 对原答案的支持/反对比例
- 补充信息和修正
- 有价值的个人经历分享
Step 5: 输出分析报告
## {问题标题}
> {回答数} 个回答 · {关注数} 人关注 · {浏览数} 次浏览
> 话题:{话题列表}
### 观点分布
**阵营 A:{观点概括}**(约 X 位答主)
- {作者1}({点赞数} 赞):{核心观点}
- {作者2}({点赞数} 赞):{核心观点}
**阵营 B:{观点概括}**(约 X 位答主)
- {作者3}({点赞数} 赞):{核心观点}
### 共识
- 大部分回答都认同的点
### 争议焦点
- 争议 1:A 认为... 但 B 认为...
- 争议 2 ...
### 高质量回答推荐
1. [{作者}的回答]({URL}) — {点赞数} 赞,{一句话推荐理由}
2. ...
### 评论区亮点
- {评论摘要}(来自 {作者} 回答下的评论)
注意事项
- 控制 API 调用次数:先看摘要判断价值,再决定是否读全文
- 回答数量多时(>100),说明话题热门,更要聚焦高赞回答
- 如果问题只有 1-2 个回答,直接读全文即可,不需要"分析观点分布"
- 保持客观中立,如实呈现各方观点
- 长回答提炼要点即可,不要大段复述原文
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 133 lines · 57 tokens per session scan A 49d09e5347c0
analyze-zhihu-question is a skill published in the GitHub repository alizeeblack-code/zhihu-mcp (4 stars, last pushed 6mo ago), licensed MIT. It adds 57 tokens to every session and 1,031 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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