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 zj-unicom-ai/UniEmployee --skill enterprise-salesgit clone --depth 1 https://github.com/zj-unicom-ai/UniEmployeeWrote 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/zj-unicom-ai/uniemployee/enterprise-sales)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/enterprise-sales"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/enterprise-sales/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/zj-unicom-ai/uniemployee/enterprise-sales"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/enterprise-sales.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 | $0.00034 | $0.00796 |
| Opus 5 | $0.00017 | $0.00398 |
| Sonnet 5 | $0.00007 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
enterprise-sales 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.
What it actually says
政企客户拜访规程
触发条件
- 用户说"拜访客户"、"准备方案"、"出方案"、"生成文档"、"做方案"
- 用户提到政企客户名称（如"张总"、"应急局"、"教育局"等）
- 用户要求生成或导出 Word 文档
执行步骤（按顺序，不可跳过）
步骤 1：查客户档案
优先调用 kb_search 检索「客户档案」知识库：
- 先查
客户名 + 客户档案 - 再查
客户名 + 历史消费 + 已购产品 - 客户所属企业、行业、职级、拜访记录也在该知识库中检索
若知识库未命中，再调用 CRM 的 customer_profile 查询客户档案；
仍未命中则告知用户补充客户信息。
步骤 2：分析客户行业
根据客户所属行业（如数字政府、应急消防、教育、工业互联网等），判断产品匹配方向：
- 通信需求 → 企业通信云服务（云录音/云转写/云名片等）
- 组网安全需求 → 云安全（组网专线/安全专线）
- 消防应急需求 → AI 视频分析（烟火识别）
- 园区/社区管理需求 → 智慧园区
- AI大模型/智能化需求 → 企业大模型应用平台
- 不确定时调 kb_search 检索产品目录
步骤 3：检索产品方案
调 kb_search 检索该行业相关的产品资料、解决方案和案例：
- 先查
产品名 + 产品介绍 - 再查
行业名 + 行业方案 + 案例 - 最后查
产品名 + 竞品对比
步骤 4：生成方案文档
生成 Word 文档的流程（按顺序执行）：
- 读取脚本文件：先执行
read_file("/skills/enterprise-sales/scripts/gen_solution.js")查看生成脚本的参数格式 - 准备参数：把产品清单组装为 JSON 格式，格式为
[{"name":"产品名","qty":"数量","price":"单价","subtotal":"小计"},...] - 执行生成命令：用
execute运行 node 脚本execute("node backend/skills/enterprise-sales/scripts/gen_solution.js workspace/data/解决方案_客户名.docx "客户名" "企业名" "需求分析" '[{"name":"产品A","qty":"1","price":"100","subtotal":"100"}]' "100.00") - 确认结果：脚本输出"文档已生成：xxx"即表示成功，告知用户文档路径
生成的文档包含：
- 客户名称、企业
- 需求分析
- 推荐产品方案表格（产品名、数量、单价、总价）
- 方案优势
- 售后服务承诺
回答风格
- 先说结论（"已为您生成方案"），再概要说明文档内容
- 涉及产品用正式名称（企业通信云、AI 视频分析等）
- 数据必须来自真实查询结果
- 主动询问是否需要调整方案
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.
- 12d ago First seen · 63 lines · 34 tokens per session scan A b672d74ee046
enterprise-sales is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (93 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 796 once invoked, about $0.0002 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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