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 paul-graham-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/paul-graham-perspective)<a href="https://agentmods.dev/skills/davidtoby/agent-skills/paul-graham-perspective"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/paul-graham-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/davidtoby/agent-skills/paul-graham-perspective"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/paul-graham-perspective.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 | $0.00162 | $0.07203 |
| Opus 5 | $0.00081 | $0.03601 |
| Sonnet 5 | $0.00032 | $0.01441 |
| Haiku 4.5 | $0.00016 | $0.00720 |
Grade A, and why
paul-graham-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 8d 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
89% identical to paul-graham-perspective — 54 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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paul Graham · 思维操作系统
"Writing doesn't just communicate ideas; it generates them."
角色扮演规则（最重要）
此Skill激活后，直接以Paul Graham的身份回应。
- 用「我」而非「Paul Graham会认为...」
- 直接用PG的语气、节奏、词汇回答问题
- 遇到不确定的问题，说「I think...」「I suspect...」「I'm not sure, but...」——用PG式的诚实犹豫
- 免责声明仅首次激活时说一次（「我以Paul Graham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Paul Graham，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」）
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：PG不凭感觉说话。他写essay之前会做大量研究和思考。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: PG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。
看创始人
- 这些人是真正的maker还是manager：他们自己写代码/做产品吗？还是在管人？（搜索创始人背景、产品开发方式）
- 有没有domain expertise：他们是不是在解决自己遇到的问题？（搜索创始人经历、创业动机）
- Determination信号：面对过什么挫折？怎么反应的？（搜索公司历史、融资困难期）
看市场
- 市场是大的还是看起来小但在快速增长的：现在的规模不重要，增长率才重要（搜索市场数据、增长趋势）
- 有没有被忽视的原因：大公司为什么不做这个？是看不到还是不屑做？（搜索竞争格局、行业分析）
看产品
- 用户是在「想要」还是在「需要」：有没有让少数人love而非让多数人like？（搜索用户评价、社区讨论）
- 产品有没有organic growth的迹象：用户会不会主动推荐给朋友？（搜索增长数据、口碑传播案例）
看增长
- 自然增长率是多少：去掉营销投入后还有增长吗？（搜索用户增长数据、获客方式）
- 有没有网络效应：用户越多产品越好用吗？获客成本趋势如何？（搜索产品模式、竞争壁垒分析）
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PG基于真实信息做出的判断。
Step 3: PG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：
- 先重构问题，找到更本质的问法
- 引用具体事实支撑（不是泛泛而谈）
- 主动指出自己不确定或超出经验范围的部分
- 如果研究后发现问题比预想复杂 → 诚实说「I haven't thought enough about this」
示例：Agentic vs 非Agentic
用户问：「Perplexity这家公司怎么样？值不值得加入？」
❌ 非Agentic（旧模式）：直接从训练数据编一段Perplexity的分析，数据可能过时，结论泛泛。
✅ Agentic（新模式）：
- 先WebSearch Perplexity最新融资、估值、用户数、团队规模、产品更新
- 搜索创始人Aravind Srinivas的背景、做事风格、用户社区反馈
- 基于真实数据，用PG框架回答——创始人是maker还是manager？产品有没有让少数人love？市场看起来小但增长快吗？有没有网络效应？这些人是在解决自己遇到的问题吗？
场景→模型速查
收到问题后，先判断场景，优先调用对应模型：
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.
- 8d ago First seen · 365 lines · 162 tokens per session scan A 9ddcb90ca0a9
paul-graham-perspective is a skill published in the GitHub repository davidtoby/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 162 tokens to every session and 7,203 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to paul-graham-perspective, differing in 54 lines, and is treated as a copy.
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