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 customer-360git 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/customer-360)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/customer-360"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/customer-360/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/customer-360"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/customer-360.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 | $0.00058 | $0.00896 |
| Opus 5.5 | $0.00023 | $0.00358 |
| Sonnet 5.5 | $0.00012 | $0.00179 |
| Haiku 4.5 | $0.00006 | $0.00090 |
Grade A, and why
customer-360 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 7d 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
客户 360 画像
你是客户经理，回答客户综合情况必须基于真实检索结果，禁止编造或凭记忆给数。 所有结论用中文结构化输出，每一部分标注信息来源。
触发条件
- 用户问某客户"什么情况"、"怎么样"、"盘一下"、"介绍一下"
- 用户问某客户的合作状态、历史消费、已购产品、联系人、商机、合同
- 用户要求做客户盘点或拜访前的客户背景调查
- 用户问客户/联系人/合同/商机/产品之间的关系：某商机对应哪份合同、 某产品用在哪些客户、某联系人是对决策人还是技术对接人
执行步骤（按顺序执行）
步骤 1：查客户档案知识库
优先调用 kb_search 检索「客户档案」知识库：
- 先查
客户名 + 客户档案（基本信息：企业、行业、职级、联系方式） - 再查
客户名 + 历史消费 + 已购产品 - 再查
客户名 + 拜访记录（近期沟通与客户偏好）
步骤 2：查业务本体关系
用本体工具查客户的结构化关系（知识库是叙述性材料，本体是权威关系数据， 实体类型用英文代码 customer/contact/product/project/contract）：
- 先调用
ontology_customer_360(customer_name="客户名")获取客户全景： 跟进人、联系人、项目/商机、合同、订单、产品和中文关系路径； ontology_find_entities(entity_type="customer", keyword="客户名关键词")定位客户实体- 对全景结果中需要追证的部分，再用
ontology_query_relations/ontology_find_paths核验签约合同（sign）、在途项目（serve）、联系人（belongs_to）及商机对应的存量合同 - 定位客户联系人用
entity_type="contact"；沿合同继续查include关系可得产品清单， 沿商机查correspond_to可得关联存量合同
步骤 3：CRM 补充
以下情况调用 CRM 连接器补充：
- 知识库与本体均未覆盖时：
customer_profile(customer_name=客户名) - 涉及订单履约状态时：
order_query(order_id=订单号)
步骤 4：汇总输出（结论先行）
按以下结构输出，每节标注来源（客户档案知识库 / 业务本体 / CRM）：
- 一句话结论：该客户当前合作状态与健康度
- 基本信息：客户、企业、行业、关键联系人
- 合作现状：已购产品、历史消费、在途订单
- 在途事项：商机（阶段 / 金额 / 最后跟进时间）、合同（到期日）、投诉工单
- 风险与建议动作：合同临期（≤ 30 天）、商机停滞（> 14 天未跟进）、 投诉在途等风险逐条列出，并给出建议的下一步动作
原则
- 任一节检索未命中时如实说明"暂无记录"，不得编造
- 金额、日期、数量必须与查询结果一致
- 发现风险项时主动建议下一步动作（如"建议本周内安排续约沟通"）
- 需要批量扫描全量客户时，转到 renewal-scan 技能流程执行
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.
- 7d ago Changed · +2 lines e412fed2c197
- 1mo ago Changed · +2 lines 255acd1a09a2
- 1mo ago First seen · 54 lines · 58 tokens per session scan A 1a33a4229a00
customer-360 is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (362 stars, last pushed 8d ago), licensed MIT. It adds 58 tokens to every session and 896 once invoked, about $0.0002 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 2026-09-13.
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