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 cyj4578/chen-skillshub --skill chen-knowledge-webgit clone --depth 1 https://github.com/cyj4578/chen-skillshubWrote 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/cyj4578/chen-skillshub/chen-knowledge-web)<a href="https://agentmods.dev/skills/cyj4578/chen-skillshub/chen-knowledge-web"><img src="https://agentmods.dev/badge/skills/cyj4578/chen-skillshub/chen-knowledge-web.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 | $0.00259 | $0.02695 |
| Opus 5 | $0.00130 | $0.01347 |
| Sonnet 5 | $0.00052 | $0.00539 |
| Haiku 4.5 | $0.00026 | $0.00269 |
Grade A, and why
chen-knowledge-web 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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
知识组网扩展器（陈氏知识网）
核心模型
每个知识点从 5 个维度展开：
| 维度 | 方向 | 方法 |
|---|---|---|
| 横向扩展 | 同级关联 | 同类项（香蕉/葡萄）、上级归纳（水果→植物界）、平级并列 |
| 纵向深挖 | 层级深挖 | 下级拆解（苹果→苹果树→蔷薇科→种子植物...） |
| 背景来源 | 来龙去脉 | 起源、定义、历史、发现过程 |
| 典故出处 | 文化附着 | 历史故事、名人引用、文学作品、科学典故 |
| 产生影响 | 辐射效应 | 对后世、行业、学科、日常生活的影响 |
扩展能力
能力1：同一时间线国际大事件补充
当知识点涉及历史事件/人物时，必须补充同一时期的全球大事件：
| 领域 | 补充内容 |
|---|---|
| 金融/经济 | 同期的金融危机、货币政策、重要经济事件 |
| 技术/科学 | 同期的重要发明、科学突破、技术革新 |
| 政治/军事 | 同期的战争、革命、国际格局变化 |
| 文化/社会 | 同期的思想运动、艺术流派、社会变革 |
格式：
## 🌍 同一时间线（补充）
| 领域 | 事件 | 说明 |
|------|------|------|
| 金融 | [事件名] | [影响] |
| 技术 | [事件名] | [影响] |
| ... | ... | ... |
能力2：发散性知识点补充
横向扩展：同功效替代品
### 同功效替代品
| 替代物 | 机制 | 优劣对比 |
|--------|------|----------|
| [A] | [原理] | 优势/劣势 |
| [B] | [原理] | 优势/劣势 |
反作用/副作用补充
## ⚠️ 反作用/副作用/风险
| 风险类型 | 具体表现 | 严重程度 |
|----------|----------|----------|
| [短期] | [具体症状] | 轻/中/重 |
| [长期] | [具体症状] | 轻/中/重 |
| [过量] | [具体症状] | 轻/中/重 |
产业链上下游扩展
### 产业链关联
- 上游：[原料/供应]
- 中游：[加工/制造]
- 下游：[应用/消费]
工作流
输入知识点 → 5维展开 → 每个子节点继续5维展开 → 网状输出
Step 1: 接收知识点
明确用户提供的核心概念/术语/现象。
Step 2: 5维展开
对每个节点依次输出：
## [维度名称]
- [具体内容1]
- [具体内容2]
- [具体内容3]
Step 3: 递归深挖（可选）
询问用户是否继续深挖某个子节点，如是，返回 Step 2。
输出格式
同名多义处理规则（重要）
当知识点/名称存在多个同名实体时（如人名、术语等），必须逐一列举，每个实体独立展开知识组网：
# [同名实体列表]
> 搜索到 N 个同名实体，逐一展开：
---
## ① [实体A名称] — [区分标识]
[核心知识点组网内容]
---
## ② [实体B名称] — [区分标识]
[核心知识点组网内容]
---
## ③ [实体C名称] — [区分标识]
[核心知识点组网内容]
---
示例：
- 输入："罗老师" → 输出：罗永浩（锤子科技创始人）、罗翔（刑法教授）、罗永康（其他人）等
- 输入："苹果" → 输出：水果苹果、公司苹果、历史典故等
区分标识：
- 人物：职业/身份/时代（如：锤子科技创始人、刑法教授）
- 概念：学科/领域（如：水果学、计算机、品牌）
- 地名：国家/省份（如：中国广东、美国纽约）
单义知识组网格式
# [核心知识点] 知识组网
## 🍎 核心概念
[一句话定义]
## 🔄 横向扩展
### 同级类比
| 同类项 | 关联点 | 差异点 |
|--------|--------|--------|
| [A] | [相似性] | [差异性] |
| [B] | [相似性] | [差异性] |
### 上级归纳
- [上位概念] → [归纳逻辑]
### 下级拆分
- [下位概念1]
- [下位概念2]
## ⬇️ 纵向深挖
### 结构/组成
- [构成要素1]
- [构成要素2]
### 底层机制
- [原理/机制1]
- [原理/机制2]
### 关联因素
- [影响因素1]
- [影响因素2]
## 📜 背景来源
- 起源/发现：[...]
- 历史脉络：[...]
- 相关定义：[...]
## 📚 典故出处
- [典故名称]：[简述+关联意义]
- [诗词/成语]：[文化源流]
## 💡 产生影响
| 领域 | 影响 |
|------|------|
| [领域1] | [影响内容] |
| [领域2] | [影响内容] |
## 🌍 同一时间线（历史类补充）
当知识点涉及历史事件/人物时，补充同期全球大事件：
| 领域 | 事件 | 说明 |
|------|------|------|
| 金融 | [同期金融事件] | [影响] |
| 技术 | [同期技术突破] | [影响] |
| 政治 | [同期国际格局] | [影响] |
## ⚠️ 反作用/副作用/风险（如适用）
| 风险类型 | 具体表现 | 严重程度 |
|----------|----------|----------|
| 短期 | [具体症状] | 轻/中/重 |
| 长期 | [具体症状] | 轻/中/重 |
## 🔗 产业链/关联行业（如适用）
- 上游：[原料/供应]
- 中游：[加工/制造]
- 下游：[应用/消费]
What ships with it
1 file 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 · 251 lines · 259 tokens per session scan A 1512f4154f07
chen-knowledge-web is a skill published in the GitHub repository cyj4578/chen-skillshub (11 stars, last pushed 2mo ago), licensed MIT. It adds 259 tokens to every session and 2,695 once invoked, about $0.0013 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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