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-answergit 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-answer)<a href="https://agentmods.dev/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-answer"><img src="https://agentmods.dev/badge/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-answer/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-answer"><img src="https://agentmods.dev/badge/skills/alizeeblack-code/zhihu-mcp/analyze-zhihu-answer.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 | $0.00049 | $0.01095 |
| Opus 5.5 | $0.00020 | $0.00438 |
| Sonnet 5.5 | $0.00010 | $0.00219 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
analyze-zhihu-answer 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 2mo 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
知乎回答深度分析
对一个具体的知乎回答进行深度分析,阅读全文并解读评论区讨论。
适用场景
- 用户给出一个知乎回答链接,想深入了解内容和评论区反应
- 用户说"帮我看看这个回答"、"这个回答评论区怎么说"
- 分析某个观点的支持/反对情况
- 评估一个回答的可信度和争议程度
工作流程
用户输入回答 URL
│
├─ Step 1: 解析 URL,提取 question_id 和 answer_id
│
├─ Step 2: 获取回答全文
│
├─ Step 3: 获取评论区内容
│
├─ Step 4: 获取所属问题的上下文(可选)
│
└─ Step 5: 输出分析报告
Step 1: 解析 URL
从用户输入中提取 ID:
- 标准格式
https://www.zhihu.com/question/12345678/answer/87654321→ question_id =12345678, answer_id =87654321 - 如果用户只给了 answer_id,需要提醒补充 question_id 或完整 URL
Step 2: 获取回答全文
get_answer_detail(question_id="12345678", answer_id="87654321")
获取:
- 所属问题标题
- 作者名、作者简介
- 回答完整内容
- 点赞数、评论数
- 发布/编辑时间
向用户简要汇报:
回答:{问题标题}
作者:{作者} — {作者简介}
{点赞数} 赞 · {评论数} 条评论
正在读取评论区...
Step 3: 获取评论区
get_comments(url="https://www.zhihu.com/question/12345678/answer/87654321", count=20)
获取评论列表,包含:
- 评论内容
- 评论作者
分析评论时关注:
- 支持/反对比例:多少评论认同作者观点,多少持反对意见
- 补充信息:评论中提供的额外事实、数据、个人经历
- 质疑与纠错:指出回答中错误或有争议的部分
- 高质量讨论:评论之间的有价值辩论
如果评论数很多(>50),可以分批获取或只看前 20 条最新评论。
Step 4: 获取问题上下文(可选)
如果需要理解回答在整个问题讨论中的位置:
get_question_detail(question_id="12345678")
了解:
- 问题本身的描述和背景
- 总共有多少回答
- 其他高赞回答的摘要(判断当前回答是否代表主流观点)
仅在以下情况执行:
- 用户要求对比其他回答
- 回答内容引用了问题描述中的信息
- 需要判断该回答在所有回答中的地位
Step 5: 输出分析报告
## 回答分析:{问题标题}
### 回答概览
- **作者**:{作者} — {作者简介}
- **数据**:{点赞数} 赞 · {评论数} 条评论
- **时间**:{发布时间}
- **链接**:{URL}
### 核心观点
1. {要点 1}
2. {要点 2}
3. {要点 3}
### 论证方式
- {作者用了什么方式支撑观点:数据、案例、逻辑推理、个人经历等}
### 评论区分析
**整体态度**:{支持为主 / 反对为主 / 两极分化 / 讨论为主}
**支持方(约 X 条)**:
- {代表性支持评论摘要}
**反对方(约 X 条)**:
- {代表性反对评论摘要}
- {反对的主要理由}
**补充信息**:
- {评论中提供的有价值补充}
**质疑与纠错**:
- {评论中指出的错误或争议点}
### 可信度评估
- 论据是否充分
- 评论区是否有有效反驳
- 作者背景是否相关
注意事项
- 回答内容可能很长,提炼核心观点即可,不需要逐段复述
- 评论区信息量大时,按主题归类而非逐条列举
- 保持客观,如实呈现支持和反对意见
- 如果回答涉及专业领域(医学、法律等),提醒用户需要专业人士验证
- 注意区分"高赞评论"和"普通评论"的权重
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.
- 2mo ago First seen · 150 lines · 49 tokens per session scan A 7cbf187ba2e8
analyze-zhihu-answer is a skill published in the GitHub repository alizeeblack-code/zhihu-mcp (5 stars, last pushed 8mo ago), licensed MIT. It adds 49 tokens to every session and 1,095 once invoked, about $0.0002 per session on Opus 5.5. 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.
analyze-zhihu-answer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| analyze-zhihu-answeralizeeblack-code/zhihu-mcp | Skill | 5 | 8mo ago | MIT | A | 49 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |