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 zjp1997720/zhijian-skills --skill enterprise-clone-buildergit clone --depth 1 https://github.com/zjp1997720/zhijian-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/zjp1997720/zhijian-skills/enterprise-clone-builder)<a href="https://agentmods.dev/skills/zjp1997720/zhijian-skills/enterprise-clone-builder"><img src="https://agentmods.dev/badge/skills/zjp1997720/zhijian-skills/enterprise-clone-builder/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/zjp1997720/zhijian-skills/enterprise-clone-builder"><img src="https://agentmods.dev/badge/skills/zjp1997720/zhijian-skills/enterprise-clone-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00197 | $0.02883 |
| Opus 5.5 | $0.00079 | $0.01153 |
| Sonnet 5.5 | $0.00039 | $0.00577 |
| Haiku 4.5 | $0.00020 | $0.00288 |
Grade A, and why
enterprise-clone-builder 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 29d 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:
- enterprise-clone-builder — 100% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Clone Builder
这是什么
企业分身的建库工具。输入企业名称和资料，输出一个完整的、标准化的分身仓库。
一句话：builder 建库，writer 写作。builder 产出的仓库，直接被 enterprise-clone-writer 消费。
使用者
智见AI交付团队（大鹏或团队成员）。使用者是专业人士，不是企业客户。
客户买的是建好的分身成品。builder 是生产工具，不是交付给客户的自助工具。
输入参数
| 参数 | 必填 | 说明 |
|---|---|---|
| 企业名称 | 是 | 全称，用于创建仓库目录和联网搜索 |
| 官网URL | 否 | 有则抓取官网，无则跳过 |
| 本地资料路径 | 否 | 客户提供的资料文件夹路径 |
| 输出路径 | 否 | 默认当前目录下创建 {企业名}-企业分身/ |
如果有本地资料路径，本地资料优先于联网调研——最好的资料永远在企业本地。
主流程
Step 0：初始化 + 本地资料盘点
0.1 创建标准目录结构
根据企业名称创建仓库根目录和全部子目录。目录规范见 references/directory-spec.md。
0.2 扫描本地资料
如果提供了本地资料路径：
- 递归扫描该目录下所有文件
- 识别文件格式（PDF / Word / Excel / PPT / 图片 / Markdown / 纯文本）
- 读取可读文件的前500字，判断内容类型
- 按内容类型归档到
02-原始素材/本地资料/下对应子目录
智能分类规则见 references/local-intake-guide.md。
无法确定类型的文件归入 本地资料/未分类/，在盘点报告中标注。
0.3 诊断覆盖度
扫描完所有文件后，输出7个维度的覆盖度报告：
| 维度 | 判定标准 |
|---|---|
| 充足 | 3个以上文件 |
| 不足 | 1-2个文件 |
| 缺失 | 0个文件 |
0.4 输出资料盘点报告
在 05-调研记录/ 下生成 资料盘点报告.md，记录扫描结果、归档情况和缺口诊断。
如果 --interactive 模式，在这里停下来，让用户确认或补充后再继续。
Step 1：联网补充调研
只补 Step 0 诊断出的缺口，不全量抓取。
- 用 web-clipper 抓取官网（如有URL）
- 官网首页
- 产品列表页 → 从中提取子链接 → 抓代表性产品详情（10-20个）
- 新闻列表页 → 从中提取子链接 → 抓代表性新闻详情（10-20条）
- 搜索企业名称，抓取第三方平台信息
- 根据缺口针对性补充：对外发声不足→重点抓新闻；产品不足→重点抓产品页
- 所有抓取文件登记到
02-原始素材/source-map.md
如果企业太小、互联网上几乎没有信息：标注"公开信息不足，分身质量依赖本地资料"，继续。
web-clipper 调用方式见 references/web-clipper-usage.md。
Step 2：结构化提取
从全部素材（本地 + 联网）提取，写入企业画像和内容资产。
2.1 企业画像 5 维度 → 01-企业画像/
| 文件 | 提取内容 |
|---|---|
| 01-基本信息.md | 工商、规模、地址、联系方式 |
| 02-产品与服务.md | 产品线分类、核心技术、应用场景 |
| 03-客户与市场.md | 目标客户、行业分布、竞争格局 |
| 04-品牌调性.md | 品牌声音、表达风格、禁用表达 |
| 05-发展历程.md | 创立时间、关键节点 |
2.2 内容资产 4 库 → 03-内容资产/
| 文件 | 提取内容 |
|---|---|
| 产品描述库.md | 每个产品标准化描述（型号/参数/用途/卖点/来源） |
| 客户案例库.md | 客户案例和应用场景 |
| 技术知识库.md | 行业背景、技术原理、标准 |
| 品牌素材.md | slogan、简介、标准文案、营销话术 |
2.3 来源标注
所有提取的信息标注来源文件。区分"有证据的"和"推断的"。写入 05-调研记录/claims-ledger.md。
详细提取规范见 references/extraction-guide.md。
Step 3：文风分析
3.1 选代表性文章
从对外发声类素材中选 3-5 篇代表性文章。优先级：本地公众号文章 > 官网新闻 > 第三方平台。
What ships with it
7 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.
- 29d ago Changed · +10 lines c7a5eeec59d2
- 2mo ago First seen · 298 lines · 197 tokens per session scan A 214730d3996f
enterprise-clone-builder is a skill published in the GitHub repository zjp1997720/zhijian-skills (787 stars, last pushed 4d ago), licensed MIT. It adds 197 tokens to every session and 2,883 once invoked, about $0.0008 per session on Opus 5.5. 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.
enterprise-clone-builder compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| enterprise-clone-builderzjp1997720/zhijian-skills | Skill | 787 | 4d ago | MIT | A | 197 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |