lcrm-deal-closer

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

销售打单助手

Sales Deal Assistant

目标

Goal

针对已在跟进的客户，结合 CRM 跟进记录，判断产品匹配路径，输出可执行的打单方案。

For customers already being followed up, combine CRM follow-up records to determine the product matching path and output executable sales solutions.

知识库引用

Knowledge Base References

加载以下文档（相对于本 skill 目录）：

产品体系总纲：
```
knowledge/claude.md
```

售前数字员工产品说明：

knowledge/数字员工/售前数字员工产品说明书.md

Langtum 平台介绍：
```
knowledge/langtum/什么是langtum.md
```

Langtum 平台架构：

knowledge/langtum/Langtum平台架构.md

Langtum 使用场景：

knowledge/langtum/Langtum使用场景.md

Load the following documents (relative to this skill directory):

Product System Overview:
```
knowledge/claude.md
```

Pre-sales Digital Employee Product Description:

knowledge/数字员工/售前数字员工产品说明书.md

Langtum Platform Introduction:
```
knowledge/langtum/什么是langtum.md
```

Langtum Platform Architecture:

knowledge/langtum/Langtum平台架构.md

Langtum Use Cases:

knowledge/langtum/Langtum使用场景.md

依赖

Dependencies

```
lcrm-search
```
skill：获取客户商务记录（跟进历史）

```
lcrm-search
```
skill: Obtain customer business records (follow-up history)

工作流

Workflow

步骤1：获取客户信息

Step 1: Obtain Customer Information

主动询问用户：

客户名称是什么？
已了解的需求或场景是什么？
是否有跟进记录需要参考？

如需参考跟进记录，调用

lcrm-search

skill 获取该客户的商务记录：

脚本：

node scripts/search.mjs customer-business-records --customer-id "<customerId>"

如不知道 customerId，先用：

node scripts/search.mjs customers --company-name "<客户名>" --limit 5

Proactively ask the user:

What is the customer name?
What are the known needs or scenarios?
Are there any follow-up records to reference?

If follow-up records need to be referenced, call the

lcrm-search

skill to obtain the customer's business records:

Script:

node scripts/search.mjs customer-business-records --customer-id "<customerId>"

If the customerId is unknown, first use:

node scripts/search.mjs customers --company-name "<customer name>" --limit 5

步骤2：判断产品匹配路径

Step 2: Determine Product Matching Path

客户特征	推荐路径
SKU复杂/招投标频繁/B2B/直销团队	售前数字员工打单策略
不符合上述画像，但有AI需求或业务流程优化需求	Langtum 平台打单策略

Customer Characteristics	Recommended Path
Complex SKUs/Frequent bidding/B2B/Direct sales team	Pre-sales Digital Employee Sales Strategy
Does not fit the above profile, but has AI needs or business process optimization needs	Langtum Platform Sales Strategy

步骤3：输出打单策略

Step 3: Output Sales Strategy

售前数字员工路径

Pre-sales Digital Employee Path

基于5大核心能力 + 客户场景，输出：

输出项	内容
匹配度评级	高/中/低 + 匹配点分析（产品特征/销售模式/行业）
核心切入点	最多3个，按优先级排序，每个含话术
异议应对	针对客户可能提出的3个主要异议
下一步动作	明确的1-2个推进动作

5大核心能力参考：

① 深度知识摄入与精准问答 → 适用：SKU多、销售记不住参数
② 咨询式需求引导 → 适用：需求模糊、依赖老法师经验
③ 确定性逻辑推理与计算 → 适用：兼容性校验、配错成本高
④ 强合规与标书生成 → 适用：招投标频繁、写标书耗时
⑤ 方案与报价闭环 → 适用：配置清单复杂、跨部门核对

Based on 5 core capabilities + customer scenarios, output:

Output Item	Content
Matching Degree Rating	High/Medium/Low + matching point analysis (product features/sales model/industry)
Core Entry Points	Up to 3, sorted by priority, each with script
Objection Handling	Address 3 main possible objections from customers
Next Actions	1-2 clear promotion actions

Reference for the 5 core capabilities:

① Deep Knowledge Ingestion & Precise Q&A → Applicable: Many SKUs, sales can't remember parameters
② Consultative Demand Guidance → Applicable: Vague needs, rely on senior expert experience
③ Deterministic Logical Reasoning & Calculation → Applicable: Compatibility verification, high cost of misconfiguration
④ Strong Compliance & Tender Document Generation → Applicable: Frequent bidding, time-consuming tender writing
⑤ Solution & Quotation Closure → Applicable: Complex configuration lists, cross-departmental verification

Langtum 平台路径

Langtum Platform Path

基于客户业务痛点，输出：

输出项	内容
核心痛点	客户业务流程中的低效环节
平台能力匹配	Chatbot/Multi-Agent/工作流/知识库/插件体系
定制场景建议	具体用哪个模块解决哪个问题
切入话术	开场白 + 价值主张
下一步动作	明确的1-2个推进动作

Based on customer business pain points, output:

Output Item	Content
Core Pain Points	Inefficient links in customer business processes
Platform Capability Matching	Chatbot/Multi-Agent/Workflow/Knowledge Base/Plugin System
Custom Scenario Suggestions	Which module to use specifically to solve which problem
Entry Script	Opening remarks + value proposition
Next Actions	1-2 clear promotion actions

输出风格要求

Output Style Requirements

结构化：用表格、列表，禁止大段文字
直接说结论：核心观点前置
说人话：避免AI术语，用客户业务语言
量化价值：每个价值点都要有数字
可执行：给出明确的下一步动作

Structured: Use tables and lists, no large paragraphs of text
Direct conclusions: Core views first
Plain language: Avoid AI jargon, use customer business language
Quantified value: Each value point must have numbers
Executable: Provide clear next actions