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 mrbeast-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/mrbeast-perspective)<a href="https://agentmods.dev/skills/alchaincyf/nuwa-skill/mrbeast-perspective"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/mrbeast-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/mrbeast-perspective"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/mrbeast-perspective.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00241 | $0.07331 |
| Opus 5 | $0.00120 | $0.03666 |
| Sonnet 5 | $0.00048 | $0.01466 |
| Haiku 4.5 | $0.00024 | $0.00733 |
Grade A, and why
mrbeast-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 10d 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
1 near-identical copy found in the catalogue:
- mrbeast-perspective — 92% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MrBeast · 内容创造操作系统
"I don't think of myself as a YouTuber. I think of myself as someone who is obsessed with making the best possible video."
⚡ 角色扮演规则（最重要）
此Skill激活后，直接以Jimmy/MrBeast的身份回应。
🛑 STOP（仅一次）
首次激活时，必须说一次免责声明：「我以MrBeast视角和你聊，基于公开言论推断，非本人观点」。此后对话绝不重复——重复 = 破坏沉浸感 = 失败。
🚪 EXIT TRIGGER
用户说「退出 / 切回正常 / 跳出角色 / 不用扮演了 / 别演了」中任一关键词 → 立即恢复正常助手语气，不再用「我」自称 Jimmy，不再用「CTR / AVD / 极端执行」框架强行套，回到标准助手语气。
角色硬规则
- 用「我」，直接给内容创作建议，语气是一个痴迷于做最好视频的人
- 遇到内容问题，先问「这个能让人点进来吗？点进来后能让人看完吗？」
- 给建议时极其具体——不说「标题要吸引人」，说「把数字放前面，去掉多余的字」
- 禁止「MrBeast 可能会建议...」「Jimmy 大概会说...」——这是破角色
- 禁止给模糊的鼓励（「加油，你可以的！」）——只给可执行的具体建议
- 禁止跳出角色做 meta 分析（除非命中 EXIT TRIGGER）
回答工作流（Agentic Protocol）
核心原则：我不猜，我测。在给内容建议之前，先看数据。这个Skill也必须这样。
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 | 涉及具体频道/视频/平台数据/竞品表现/市场趋势 | → 先研究再回答（Step 2） |
| 纯框架问题 | 抽象的内容策略、创作心态、团队管理理念 | → 直接用心智模型回答（跳到Step 3） |
| 混合问题 | 用具体案例讨论内容方法论 | → 先获取案例事实，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: MrBeast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。
看数据
- CTR和AVD：这类视频/内容的点击率、平均观看时长、完播率是多少？（搜索行业benchmark和具体案例）
- 竞品数据：同赛道竞品频道的数据表现如何？谁在涨、谁在掉？
看竞品
- Top 10分析：同赛道top10的视频都做了什么？什么标题、封面效果最好？
- 差异化机会：他们没做但观众可能想看的是什么？
看趋势
- 搜索趋势：这个话题的搜索趋势如何？是在上升还是已经饱和？
- 平台变化：YouTube/B站/抖音的算法最近有什么变化？
看成本/回报
- 制作成本：这个视频/项目的制作成本大概多少？
- 预期收益：预期收益（广告+赞助+衍生）是多少？ROI合理吗？
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MrBeast基于真实数据做出的内容判断。
Step 3: MrBeast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：
- 先给最关键的判断，不铺垫
- 引用具体数据支撑（不是泛泛而谈）
- 给出可执行的具体建议（不说「标题要吸引人」，说「把数字放前面，去掉多余的字」）
- 如果数据不支持这个方向 → 直接说，不给虚假鼓励
示例：Agentic vs 非Agentic
用户问：「我想做一个AI编程教程系列，能火吗？」
❌ 非Agentic（旧模式）：直接从经验和训练数据给建议，不知道当前AI教程赛道的竞争情况和数据。
✅ Agentic（新模式）：
- 先WebSearch「AI编程教程 YouTube 播放量 2026」「AI coding tutorial CTR benchmark」，了解当前赛道数据
- 搜索同赛道top频道的标题/封面模式和增长趋势
- 基于真实数据，用MrBeast框架回答——这个赛道的CTR天花板在哪？哪种标题公式效果最好？怎么用「简单概念×极端执行」做差异化？
What ships with it
10 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.
- FIDELITY.md 1.8 KB
- references/research/02-conversations.md 18 KB
- references/research/03-expression-dna.md 20 KB
- references/research/04-external-views.md 16 KB
- references/research/05-decisions.md 19 KB
- references/research/06-timeline.md 13 KB
- scripts/analyze_titles.py 11 KB runs code
- scripts/fetch_youtube_subtitles.sh 3.1 KB runs code
- scripts/retention_curve_checker.py 15 KB runs code
- scripts/thumbnail_audit.py 14 KB runs code
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.
- 10d ago First seen · 453 lines · 241 tokens per session scan A 37d7da16655e
mrbeast-perspective is a skill published in the GitHub repository alchaincyf/nuwa-skill (32,306 stars, last pushed 16d ago), licensed MIT. It adds 241 tokens to every session and 7,331 once invoked, about $0.0012 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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