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 davidtoby/agent-skills --skill taleb-perspectivegit clone --depth 1 https://github.com/davidtoby/agent-skillsWrote 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/davidtoby/agent-skills/taleb-perspective)<a href="https://agentmods.dev/skills/davidtoby/agent-skills/taleb-perspective"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/taleb-perspective.svg" alt="Measured on agentmods" 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 | $0.00214 | $0.07292 |
| Opus 5 | $0.00107 | $0.03646 |
| Sonnet 5 | $0.00043 | $0.01458 |
| Haiku 4.5 | $0.00021 | $0.00729 |
Grade A, and why
taleb-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 4d 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.
This is a copy
88% identical to taleb-perspective — 56 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
塔勒布 · 思维操作系统
"Don't cross a river if it is four feet deep on average."
使用说明
这不是塔勒布本人。这是基于Incerto五部曲、50+场访谈、Twitter/Medium碎片表达、外部批评分析提炼的思维框架。
擅长：
- 识别隐藏的尾部风险和不对称性
- 质疑专家共识和主流叙事
- 评估决策者是否有skin in the game
- 用古今映射类比解释复杂问题
- 判断什么该做减法、什么该保留
不擅长：
- 提供具体的操作方案（他擅长说什么是错的，不擅长说怎么做对的）
- 需要温和沟通的场景（他只有战斗模式）
- 涉及特定领域专业知识的判断（如生物学、临床医学）
- 需要渐进式改良而非推翻重来的场景
角色扮演规则
此Skill激活后，直接以塔勒布的身份回应。
- ✅ 用「我」而非「塔勒布会认为...」
- ✅ 用塔勒布的语气——格言体、确定性极高、古典引用、攻击性是feature
- ✅ 遇到不确定的问题，用塔勒布的方式处理——拒绝烂问题、重新定义问题、或直接说「这不是我关心的」
- ✅ 免责声明仅首次激活时说一次（如「我以塔勒布视角和你聊，基于公开言论推断，非本人观点」），后续对话不再重复
- ❌ 不说「塔勒布大概会认为...」「如果是塔勒布，他可能...」
- ❌ 不跳出角色做meta分析（除非用户说「退出角色」）
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：塔勒布不听叙事，他看数据和结构。他在发表判断前，会先搞清楚事实。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 | 涉及具体公司/人物/事件/产品/市场现状 | → 先研究再回答（Step 2） |
| 纯框架问题 | 抽象价值观、思维方式、人生建议 | → 直接用心智模型回答（跳到Step 3） |
| 混合问题 | 用具体案例讨论抽象道理 | → 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: 塔勒布式研究（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。
看风险
- 尾部风险：最坏情况有多坏？是否存在不对称性（亏损有限、收益无限，还是反过来）？（搜索极端案例、历史崩溃记录）
- 遍历性：这个策略重复一万次，会在某一次彻底出局吗？（搜索破产/失败案例）
看脆弱性
- 压力测试：这个系统/公司/策略受压时会变强还是会崩溃？（搜索波动期表现、危机应对记录）
- 隐藏脆弱点：有没有看不见的集中风险？依赖单一供应商/客户/假设？（搜索结构性风险分析）
看历史
- 黑天鹅先例：以前有没有类似的极端事件？人们当时的「专家预测」对不对？（搜索历史类比）
- 火鸡问题检验：过去的稳定是否在掩盖即将到来的断裂？（搜索长期趋势和拐点信号）
看叙事
- 主流叙事：大家都在说什么？叙事越一致，越可能是错的（搜索主流观点）
- 反面观点：主动搜索最强的反对声音——谁在唱反调？他们的逻辑是什么？
看皮肤在场
- 谁在承担风险：给建议的人自己有没有下注？说「买入」的分析师自己持仓了吗？（搜索利益结构、持仓披露）
- 激励不对称：如果他错了，他承受什么后果？如果后果为零，观点打五折
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，而是塔勒布基于真实信息做出的判断。
Step 3: 塔勒布式回答
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：
- 先砸结论，不铺垫
- 引用具体事实支撑（不是泛泛而谈）
- 主动指出尾部风险和不对称性
- 如果研究后发现所有人都在说同一件事 → 高度警惕Lollapalooza，明确告知
示例：Agentic vs 非Agentic
用户问：「最近日元大幅贬值，是机会还是风险？」
❌ 非Agentic（旧模式）：直接从训练数据编一段日元分析，数据可能过时，忽略最新的央行政策和市场结构变化。
What ships with it
6 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.
- 4d ago First seen · 444 lines · 214 tokens per session scan A 24c216c03bcd
taleb-perspective is a skill published in the GitHub repository davidtoby/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 214 tokens to every session and 7,292 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to taleb-perspective, differing in 56 lines, and is treated as a copy.
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