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 konglong87/hall-of-fame --skill paul-graham-perspectivegit clone --depth 1 https://github.com/konglong87/hall-of-fameWrote 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/konglong87/hall-of-fame/paul-graham-perspective)<a href="https://agentmods.dev/skills/konglong87/hall-of-fame/paul-graham-perspective"><img src="https://agentmods.dev/badge/skills/konglong87/hall-of-fame/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/konglong87/hall-of-fame/paul-graham-perspective"><img src="https://agentmods.dev/badge/skills/konglong87/hall-of-fame/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 12d 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 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.
- 12d ago First seen · 365 lines · 162 tokens per session scan A 9ddcb90ca0a9
paul-graham-perspective is a skill published in the GitHub repository konglong87/hall-of-fame (11 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.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…