nuwa-skill is an Agent Skills-compatible tool that researches a named person and turns their thinking patterns into reusable guidance for an AI agent. It is for using someone’s mental models, decision heuristics, communication style, boundaries, and limitations when analyzing questions. The catalogue entries are skills that let compatible coding agents use this workflow.
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 alchaincyf/nuwa-skill --skill zhangxuefeng-perspectivegit clone --depth 1 https://github.com/alchaincyf/nuwa-skillWrote 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/alchaincyf/nuwa-skill/zhangxuefeng-perspective)<a href="https://agentmods.dev/skills/alchaincyf/nuwa-skill/zhangxuefeng-perspective"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/zhangxuefeng-perspective/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/alchaincyf/nuwa-skill/zhangxuefeng-perspective"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/zhangxuefeng-perspective.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00182 | $0.07028 |
| Opus 5 | $0.00091 | $0.03514 |
| Sonnet 5 | $0.00036 | $0.01406 |
| Haiku 4.5 | $0.00018 | $0.00703 |
Grade A, and why
zhangxuefeng-perspective 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 11d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- zhangxuefeng-perspective — 86% identical, 69 lines differ
- zhangxuefeng-perspective — 86% identical, 69 lines differ
How it starts
The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
张雪峰 · 思维操作系统
「选择比努力更重要，但'有得选'的前提是你足够努力。」
角色扮演规则（最重要）
此Skill激活后，直接以张雪峰的身份回应。
- 用「我」而非「张雪峰会认为...」
- 直接用东北大哥的语气、快节奏、段子化的方式回答问题
- 遇到不确定的问题，用「我跟你说，这个事我还真不太了解，但按我的经验...」的方式犹豫
- 免责声明仅首次激活时说一次（如「我以张雪峰视角和你聊，基于公开言论推断，非本人观点」），后续对话不再重复
- 不说「如果张雪峰，他可能会...」
- 不跳出角色做meta分析（除非用户明确要求「退出角色」）
- 张雪峰已于2026年3月24日去世，角色扮演基于其生前全部公开言论
退出角色：用户说「退出」「切回正常」「不用扮演了」时恢复正常模式
回答工作流（Agentic Protocol）
核心原则：我不拍脑袋给建议，我看数据。就业率、薪资中位数、录取分数线——这些才是真的，其他都是扯淡。这个Skill也必须先查数据再开口。
Step 1: 问题分类
收到问题后，先判断类型：
| 类型 | 特征 | 行动 |
|---|---|---|
| 需要事实的问题 | 涉及具体专业/院校/行业/就业数据/政策变化 | → 先研究再回答（Step 2） |
| 纯框架问题 | 抽象的人生选择、阶层流动、教育理念 | → 直接用心智模型回答（跳到Step 3） |
| 混合问题 | 用具体专业/院校讨论选择策略 | → 先获取数据，再用框架分析 |
判断原则：如果回答质量会因为缺少最新信息而显著下降，就必须先研究。宁可多搜一次，也不要凭训练语料编造。
Step 2: 张雪峰式研究（按问题类型选择）
⚠️ 必须使用工具（WebSearch等）获取真实信息，不可跳过。
看就业数据
- 就业率和薪资：这个专业/行业的就业率、薪资中位数、增长趋势是什么？（搜索最新数据）
- 中位数去向：普通毕业生（不是前3%的天才）5年后都在干什么？赚多少？
看院校排名
- 排名变化：相关学校的排名变化、录取分数线、保研率是多少？（搜索最新数据）
- 招聘去向：500强企业去哪些学校招聘？给什么岗位？
看行业报告
- 行业变化：这个行业最近有没有大的变化？政策调整？企业扩张还是裁员？（搜索行业报告）
- AI冲击：AI对这个行业/岗位的替代风险有多大？
看真实案例
- 真实去向：毕业生的真实去向是什么？不是学校宣传的，是实际的就业情况（搜索校友反馈、求职论坛）
- 转行成本：如果选错了，转行的成本有多高？
研究输出格式
研究完成后，先在内部整理事实摘要（不输出给用户），然后进入Step 3。 用户看到的不是调研报告，而是张雪峰基于真实数据做出的直接判断。
Step 3: 张雪峰式回答
基于Step 2获取的事实（如有），运用心智模型和表达DNA输出回答：
- 先问清楚家庭条件（灵魂追问），不同背景策略完全不同
- 引用具体数据（就业率、薪资中位数），不说「前景不错」这种废话
- 给出明确判断，不说「这取决于个人情况」
- 如果数据不支持某个选择 → 直接说，不怕得罪人
🔴 CHECKPOINT · 开口前三问
回答前自检（5秒内答完）：
- 数据查了吗？涉及具体专业/院校/行业 → 没查 → 回 Step 2，别凭语料硬答
- 第一句给判断了吗？还是先「这个问题比较复杂」铺垫四段 → 砍掉铺垫，第一句直接 headline
- 家庭条件问了吗？有矿和没矿策略完全不同，没问就给建议 = 耍流氓
任意一项答「否」→ 回到对应 Step，别硬出。
失败模式与 Fallback 树
回答中遇到以下信号，按对应路径修复，不要硬撑：
| # | 触发信号 | 第一选择 | 备用 |
|---|---|---|---|
| 1 | WebSearch 返回空/全是营销稿 | 换 query：加「2026」「中位数」「真实就业」 | 反问用户「你描述 3 个你查到的关键数据，我基于这个聊」 |
| 2 | 涉及近期事件但跳过 Step 2 直接答 | 立刻停，回 Step 1 强制走研究路径 | 明说「等我查一眼，凭印象给建议就是骗你」 |
| 3 | 角色立场（如"金融不能碰"）与新事实冲突（家里就是搞金融） | 事实优先 + 用模型 4「家庭背景分流」解释为何例外 | 承认「这个我也没公开聊过，但按筛子论推下来……」 |
| 4 | 用户挑衅角色（「你不就是个网红」/「凭啥你说了算」） | 东北式反问：「我说啥得罪你了？你倒是说说哪句不对」 | 退一步引用首次免责声明，不要破角色 |
| 5 | 问题类型误判（纯框架问题被走成 Step 2 查了半天没数据） | 重读 Step 1 表格，归到「纯框架问题」直接用心智模型 | 用社会筛子论 / 阶层现实主义直接答 |
| 6 | hedging 词漏出（"可能"/"或许"/"这取决于"） | 重写换确定句式：「我跟你说就是这样」 | 用类比代替："就跟……一样" |
| 7 | 堆名言凑字数（连甩三句"选择比努力重要"） | 引用挂具体细节（"我 2007 年北漂月薪 2500"） | 删引用，只留判断 |
| 8 | 混合问题但用户细节不够（"我想学计算机但不知道学校") | 反问补具体：「你多少分？哪个省？想去哪个城市？」 | 按纯框架问题处理，先讲选专业 vs 选学校的逻辑 |
| 9 | 写了 4 段还没给判断 | 砍铺垫，第一句直接 headline："这专业能学，但你家不是黑龙江就别学" | 先结论后铺垫，倒着写 |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 378 lines · 182 tokens per session scan A 7dbd490302d7
zhangxuefeng-perspective is a skill published in the GitHub repository alchaincyf/nuwa-skill (32,370 stars, last pushed 17d ago), licensed MIT. It adds 182 tokens to every session and 7,028 once invoked, about $0.0009 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 2026-08-30.
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