inbound-lead-qualification

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English

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Chinese

Inbound Lead Qualification

入站线索资格审核

Takes a set of inbound leads and validates each against your full ICP criteria. Not a fast-pass triage (that's

inbound-lead-triage

) — this is the thorough qualification step that determines whether a lead is genuinely worth pursuing, and produces a scored CSV for the team.

接收一组入站线索，并根据您的完整ICP标准对每条线索进行验证。这并非快速分流（快速分流是

inbound-lead-triage

的功能）——这是全面的资格审核步骤，用于判断线索是否真正值得跟进，并为团队生成带评分的CSV文件。

When to Auto-Load

自动加载时机

Load this composite when:

User says "qualify these inbound leads", "check if these leads are ICP", "score my inbound"
An upstream triage has been completed and leads need deeper qualification
User has a batch of leads and wants a qualified/disqualified verdict on each

在以下场景加载此组合工具：

用户提出“审核这些入站线索”、“检查这些线索是否符合ICP”、“为我的入站线索评分”等需求
上游分流已完成，线索需要更深入的资格审核
用户有一批线索，希望得到每条线索的合格/不合格判定

Architecture

架构

[Inbound Leads] → Step 1: Load ICP & Config → Step 2: CRM/Pipeline Check → Step 3: Company Qualification → Step 4: Person Qualification → Step 5: Use Case Fit → Step 6: Score & Verdict → Step 7: Output CSV

[入站线索] → 步骤1：加载ICP与配置 → 步骤2：CRM/销售管道检查 → 步骤3：公司资格审核 → 步骤4：联系人资格审核 → 步骤5：用例匹配度评估 → 步骤6：评分与判定 → 步骤7：输出CSV

Step 0: Configuration (Once Per Client)

步骤0：配置（每位客户仅需一次）

On first run, establish the ICP definition and CRM access. Save to

clients/<client-name>/config/lead-qualification.json

json

{
  "icp_definition": {
    "company_size": {
      "min_employees": null,
      "max_employees": null,
      "sweet_spot": "",
      "notes": ""
    },
    "industry": {
      "target_industries": [],
      "excluded_industries": [],
      "notes": ""
    },
    "use_case": {
      "primary_use_cases": [],
      "secondary_use_cases": [],
      "anti_use_cases": [],
      "notes": ""
    },
    "company_stage": {
      "target_stages": [],
      "excluded_stages": [],
      "notes": ""
    },
    "geography": {
      "target_regions": [],
      "excluded_regions": [],
      "notes": ""
    }
  },
  "buyer_personas": [
    {
      "name": "",
      "titles": [],
      "seniority_levels": [],
      "departments": [],
      "is_economic_buyer": false,
      "is_champion": false,
      "is_user": false
    }
  ],
  "hard_disqualifiers": [],
  "hard_qualifiers": [],
  "crm_access": {
    "tool": "Supabase | HubSpot | Salesforce | CSV export | none",
    "access_method": "",
    "tables_or_objects": []
  },
  "existing_customer_source": {
    "tool": "Supabase | CRM | CSV | none",
    "access_method": ""
  },
  "qualification_prompt_path": "path/to/lead-qualification/prompt.md or null"
}

If
lead-qualification
capability already has a saved qualification prompt: Reference it directly — don't rebuild ICP criteria from scratch.

On subsequent runs: Load config silently.

首次运行时，确定ICP定义和CRM访问权限。将配置保存至

clients/<client-name>/config/lead-qualification.json

。

json

{
  "icp_definition": {
    "company_size": {
      "min_employees": null,
      "max_employees": null,
      "sweet_spot": "",
      "notes": ""
    },
    "industry": {
      "target_industries": [],
      "excluded_industries": [],
      "notes": ""
    },
    "use_case": {
      "primary_use_cases": [],
      "secondary_use_cases": [],
      "anti_use_cases": [],
      "notes": ""
    },
    "company_stage": {
      "target_stages": [],
      "excluded_stages": [],
      "notes": ""
    },
    "geography": {
      "target_regions": [],
      "excluded_regions": [],
      "notes": ""
    }
  },
  "buyer_personas": [
    {
      "name": "",
      "titles": [],
      "seniority_levels": [],
      "departments": [],
      "is_economic_buyer": false,
      "is_champion": false,
      "is_user": false
    }
  ],
  "hard_disqualifiers": [],
  "hard_qualifiers": [],
  "crm_access": {
    "tool": "Supabase | HubSpot | Salesforce | CSV export | none",
    "access_method": "",
    "tables_or_objects": []
  },
  "existing_customer_source": {
    "tool": "Supabase | CRM | CSV | none",
    "access_method": ""
  },
  "qualification_prompt_path": "path/to/lead-qualification/prompt.md or null"
}

**如果

lead-qualification

功能已保存有资格审核提示词：**直接引用该提示词——无需从头重建ICP标准。

**后续运行时：**静默加载配置。

Step 1: Load ICP Criteria & Parse Leads

步骤1：加载ICP标准并解析线索

Process

流程

Load the client's ICP config (or qualification prompt from
```
lead-qualification
```
capability)
Parse the inbound lead list — accept any format:
- Output from
```
inbound-lead-triage
```
  (already normalized)
- Raw CSV with any column structure
- Pasted list of names/emails/companies
- CRM export
Identify what data is available vs. missing per lead:
- Have: Fields present in the input
- Need: Fields required for qualification but missing
- Gap report: "X leads have company name, Y have title, Z have nothing but email"

加载客户的ICP配置（或来自
```
lead-qualification
```
功能的资格审核提示词）
解析入站线索列表——支持任意格式：
- ```
inbound-lead-triage
```
  的输出结果（已标准化）
- 任意列结构的原始CSV
- 粘贴的姓名/邮箱/公司列表
- CRM导出文件
识别每条线索的已有数据与缺失数据：
- **已有：**输入中存在的字段
- **需要：**资格审核所需但缺失的字段
- 差距报告：“X条线索有公司名称，Y条有职位，Z条仅提供了邮箱”

Output

输出

Parsed lead list with available/missing field inventory
Gap report for the user

包含已有/缺失字段清单的解析后线索列表
给用户的差距报告

Human Checkpoint

人工检查点

If >50% of leads are missing critical fields (company name or person title), recommend running

inbound-lead-enrichment

first. Ask: "Many leads are missing company/title data. Want me to enrich them first, or qualify with what's available?"

如果超过50%的线索缺失关键字段（公司名称或联系人职位），建议先运行

inbound-lead-enrichment

。询问用户：“许多线索缺失公司/职位数据。是否需要先补全数据，还是基于现有数据进行审核？”

Step 2: CRM & Pipeline Check

步骤2：CRM与销售管道检查

Process

流程

For each lead, check against existing data sources to identify overlaps:

Check 1 — Existing customer?

Search customer database by company domain/name
If match found: Flag as
```
existing_customer
```
with customer details (plan, account owner, contract status)
This is NOT a disqualification — it's a routing flag (upsell vs. new business)

Check 2 — Already in pipeline?

Search CRM/Supabase for the company in active deals
If match found: Flag as
```
in_pipeline
```
with deal details (stage, owner, last activity)
Critical: Sales rep should know before reaching out that a colleague already has this account

Check 3 — Previous engagement?

Search outreach logs for the email/company
If match found: Flag as
```
previously_contacted
```
with history summary (when, what channel, outcome)

Check 4 — Known from signal composites?

Search Supabase signals table for the company
If match found: Flag as
```
signal_flagged
```
with signal type and date

针对每条线索，检查现有数据源以识别重叠情况：

检查1 — 是否为现有客户？

通过公司域名/名称搜索客户数据库
若找到匹配项：标记为
```
existing_customer
```
，并附带客户详情（套餐、账户负责人、合同状态）
这并非不合格标记——而是路由标记（交叉销售 vs 新客户开发）

检查2 — 是否已在销售管道中？

在CRM/Supabase中搜索该公司的活跃交易
若找到匹配项：标记为
```
in_pipeline
```
，并附带交易详情（阶段、负责人、最后活动时间）
关键提示：销售代表在联系前应知晓已有同事跟进该客户

检查3 — 是否有过历史互动？

搜索外呼日志中的邮箱/公司信息
若找到匹配项：标记为
```
previously_contacted
```
，并附带历史摘要（时间、渠道、结果）

检查4 — 是否来自信号组合工具的标记？

在Supabase信号表中搜索该公司
若找到匹配项：标记为
```
signal_flagged
```
，并附带信号类型和日期

Output

输出

Each lead tagged with:

pipeline_status

new

existing_customer

in_pipeline

previously_contacted

```
pipeline_detail
```
: One sentence explaining the overlap (or null)
```
signal_flags
```
: Any signal composite matches

每条线索将被标记：

pipeline_status

new

existing_customer

in_pipeline

previously_contacted

```
pipeline_detail
```
: 说明重叠情况的一句话（无则为null）
```
signal_flags
```
: 所有信号组合工具的匹配项

Handling Overlaps

重叠情况处理

Existing customer: Don't disqualify. Mark separately. Might be expansion/upsell.
In pipeline: Don't disqualify. Flag for sales rep coordination. Note the existing deal owner.
Previously contacted but no response: Still qualify. The inbound signal means they're now warmer.
Previously contacted and rejected: Still qualify the inbound. People change their minds. Note the prior context.

**现有客户：**不判定为不合格，单独标记，可能存在拓展/交叉销售机会
**已在销售管道：**不判定为不合格，标记以便销售代表协调，注明现有交易负责人
**曾联系但无回应：**仍进行资格审核，入站信号表明客户现在意向更高
**曾联系且被拒绝：**仍对入站线索进行资格审核，客户可能改变主意，注明历史背景

Step 3: Company Qualification

步骤3：公司资格审核

Process

流程

For each lead's company, evaluate against every ICP company dimension:

Dimension 1 — Company Size

Check employee count against ICP range
Sources: enrichment data, LinkedIn company page, web search
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```
Note: If the lead is from a subsidiary or division, evaluate the relevant unit, not the parent company

Dimension 2 — Industry

Check against target and excluded industry lists
Be smart about classification: "AI-powered HR platform" matches both "AI/ML" and "HR Tech"
Score:
```
match
```
|
```
adjacent
```
(related but not core target) |
```
mismatch
```
|
```
unknown
```

Dimension 3 — Company Stage

Seed, Series A, Series B+, Growth, Public, Bootstrapped
Sources: Crunchbase, news, enrichment data
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

Dimension 4 — Geography

Check HQ location and/or the specific person's location
For remote-first companies, check where the majority of the team is
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

Dimension 5 — Use Case Fit

Based on what the company does, could they plausibly use the product?
This is the most nuanced dimension — requires understanding both the product and the company's operations
Sources: company website, product description, job postings (hint at internal tools/processes)

Score:

strong_fit

moderate_fit

weak_fit

no_fit

unknown

针对每条线索对应的公司，根据ICP的所有公司维度进行评估：

维度1 — 公司规模

将员工数量与ICP范围对比
数据来源：补全数据、LinkedIn公司主页、网络搜索
评分：
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```
注意：若线索来自子公司或分部门，评估对应业务单元而非母公司

维度2 — 行业

与目标行业和排除行业列表对比
智能分类：“AI驱动的人力资源平台”同时匹配“AI/ML”和“人力资源科技”
评分：
```
match
```
|
```
adjacent
```
（相关但非核心目标） |
```
mismatch
```
|
```
unknown
```

维度3 — 公司阶段

种子轮、A轮、B+轮、成长期、上市公司、自盈利
数据来源：Crunchbase、新闻、补全数据
评分：
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

维度4 — 地域

检查总部位置和/或联系人所在位置
对于远程优先公司，检查团队主要分布区域
评分：
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

维度5 — 用例匹配度

根据公司业务判断其是否可能使用产品
这是最精细的维度——需要同时理解产品和公司运营
数据来源：公司官网、产品描述、招聘信息（可暗示内部工具/流程）

评分：

strong_fit

moderate_fit

weak_fit

no_fit

unknown

Output

输出

Each lead gets a

company_qualification

block:

{
  "company_size": { "score": "", "value": "", "reasoning": "" },
  "industry": { "score": "", "value": "", "reasoning": "" },
  "stage": { "score": "", "value": "", "reasoning": "" },
  "geography": { "score": "", "value": "", "reasoning": "" },
  "use_case": { "score": "", "value": "", "reasoning": "" },
  "company_verdict": "qualified | borderline | disqualified | insufficient_data"
}

每条线索将生成一个

company_qualification

块：

{
  "company_size": { "score": "", "value": "", "reasoning": "" },
  "industry": { "score": "", "value": "", "reasoning": "" },
  "stage": { "score": "", "value": "", "reasoning": "" },
  "geography": { "score": "", "value": "", "reasoning": "" },
  "use_case": { "score": "", "value": "", "reasoning": "" },
  "company_verdict": "qualified | borderline | disqualified | insufficient_data"
}

Step 4: Person Qualification

步骤4：联系人资格审核

Process

流程

For each lead's contact person, evaluate against buyer persona criteria:

Dimension 1 — Title/Role Match

Check title against buyer persona title lists
Handle variations: "VP of Marketing" = "Vice President, Marketing" = "VP Marketing"
Be smart about title inflation at small companies (a "Director" at a 10-person startup ≠ "Director" at a 10,000-person enterprise)

Score:

exact_match

close_match

adjacent

mismatch

unknown

Dimension 2 — Seniority Level

Map to: Individual Contributor, Manager, Director, VP, C-Level, Founder
Check against ICP seniority requirements
Score:
```
match
```
|
```
too_junior
```
|
```
too_senior
```
|
```
unknown
```

Dimension 3 — Department

Engineering, Product, Marketing, Sales, Operations, Finance, HR, etc.
Check against ICP department targets
Score:
```
match
```
|
```
adjacent
```
|
```
mismatch
```
|
```
unknown
```

Dimension 4 — Authority Type

Based on title + seniority, classify:
- ```
economic_buyer
```
  — Can sign the check
- ```
champion
```
  — Wants it, can influence the decision
- ```
user
```
  — Would use it daily, can validate need
- ```
evaluator
```
  — Tasked with research, limited decision power
- ```
gatekeeper
```
  — Can block but not approve
- ```
unknown
```

Dimension 5 — Right Person, Wrong Company (or Vice Versa)

If company qualifies but person doesn't: Flag as
```
right_company_wrong_person
```
— this is a referral opportunity
If person qualifies but company doesn't: Flag as
```
right_person_wrong_company
```
— rare for inbound, but possible with job changers

针对每条线索的联系人，根据买方画像标准进行评估：

维度1 — 职位/角色匹配度

将职位与买方画像的职位列表对比
处理变体：“VP of Marketing” = “Vice President, Marketing” = “VP Marketing”
智能处理小公司的职位膨胀（10人初创公司的“总监” ≠ 10000人企业的“总监”）

评分：

exact_match

close_match

adjacent

mismatch

unknown

维度2 — 职级

映射为：个人贡献者、经理、总监、副总裁、高管、创始人
与ICP的职级要求对比
评分：
```
match
```
|
```
too_junior
```
|
```
too_senior
```
|
```
unknown
```

维度3 — 部门

工程、产品、营销、销售、运营、财务、人力资源等
与ICP的目标部门对比
评分：
```
match
```
|
```
adjacent
```
|
```
mismatch
```
|
```
unknown
```

维度4 — 权限类型

根据职位+职级分类：
- ```
economic_buyer
```
  — 可决策付款
- ```
champion
```
  — 认可产品，可影响决策
- ```
user
```
  — 日常使用产品，可验证需求
- ```
evaluator
```
  — 负责调研，决策权限有限
- ```
gatekeeper
```
  — 可阻碍但无法批准
- ```
unknown
```

维度5 — 公司匹配但联系人不匹配（或反之）

若公司合格但联系人不合格：标记为
```
right_company_wrong_person
```
——存在转介绍机会
若联系人合格但公司不合格：标记为
```
right_person_wrong_company
```
——入站线索中少见，但可能出现在跳槽用户中

Output

输出

Each lead gets a

person_qualification

block:

{
  "title_match": { "score": "", "value": "", "reasoning": "" },
  "seniority": { "score": "", "value": "", "reasoning": "" },
  "department": { "score": "", "value": "", "reasoning": "" },
  "authority_type": "",
  "person_verdict": "qualified | borderline | disqualified | insufficient_data",
  "mismatch_type": "null | right_company_wrong_person | right_person_wrong_company"
}

每条线索将生成一个

person_qualification

块：

{
  "title_match": { "score": "", "value": "", "reasoning": "" },
  "seniority": { "score": "", "value": "", "reasoning": "" },
  "department": { "score": "", "value": "", "reasoning": "" },
  "authority_type": "",
  "person_verdict": "qualified | borderline | disqualified | insufficient_data",
  "mismatch_type": "null | right_company_wrong_person | right_person_wrong_company"
}

Step 5: Use Case Fit Assessment

步骤5：用例匹配度评估

Process

流程

This step connects the company's likely needs to your product's actual capabilities. It goes deeper than Step 3's company-level use case check.

Infer the lead's intent from their inbound action:
- Demo request message → What did they say they need?
- Content downloaded → What topic were they researching?
- Webinar attended → What problem were they trying to solve?
- Free trial signup → What feature did they try first?
- Chatbot conversation → What questions did they ask?
Map intent to product capabilities:
- Does the product actually solve what they seem to need?
- Is this a primary use case or a stretch?
- Are there known limitations that would disappoint them?
Assess implementation feasibility:
- Based on company size and stage, can they realistically implement?
- Do they likely have the technical resources / team to adopt?
- Any known blockers for companies like this? (e.g., "banks need SOC2 and we don't have it yet")

此步骤将公司的潜在需求与产品的实际能力关联起来，比步骤3中的公司级用例检查更深入。

从入站行为推断线索意向：
- 演示请求信息 → 他们提到了哪些需求？
- 下载的内容 → 他们在研究什么主题？
- 参加的研讨会 → 他们试图解决什么问题？
- 免费试用注册 → 他们首先尝试了哪个功能？
- 聊天机器人对话 → 他们问了哪些问题？
将意向映射到产品能力：
- 产品是否真的能解决他们的潜在需求？
- 这是核心用例还是延伸用例？
- 是否存在已知的限制可能导致客户失望？
评估实施可行性：
- 根据公司规模和阶段，他们是否能实际实施？
- 他们是否可能拥有技术资源/团队来采用产品？
- 此类公司是否存在已知障碍？（例如：“银行需要SOC2认证，但我们目前没有”）

Output

输出

{
  "inferred_intent": "",
  "intent_source": "",
  "product_fit": "strong | moderate | weak | unknown",
  "product_fit_reasoning": "",
  "implementation_feasibility": "easy | moderate | complex | unlikely",
  "known_blockers": []
}

{
  "inferred_intent": "",
  "intent_source": "",
  "product_fit": "strong | moderate | weak | unknown",
  "product_fit_reasoning": "",
  "implementation_feasibility": "easy | moderate | complex | unlikely",
  "known_blockers": []
}

Step 6: Score & Verdict

步骤6：评分与判定

Scoring Logic

评分逻辑

Combine all dimensions into a final qualification verdict.

Composite Score Calculation:

Dimension	Weight	Possible Values
Company Size	15%	match=100, borderline=50, mismatch=0, unknown=30
Industry	20%	match=100, adjacent=60, mismatch=0, unknown=30
Company Stage	10%	match=100, borderline=50, mismatch=0, unknown=30
Geography	10%	match=100, borderline=50, mismatch=0, unknown=30
Use Case Fit	25%	strong=100, moderate=60, weak=20, no_fit=0, unknown=30
Person Title/Role	15%	exact=100, close=75, adjacent=40, mismatch=0, unknown=30
Person Seniority	5%	match=100, too_junior=20, too_senior=60, unknown=30

Hard overrides (bypass the score):

Any hard disqualifier present →
```
disqualified
```
regardless of score
Any hard qualifier present →
```
qualified
```
regardless of score (but still show the full breakdown)
Existing customer → Route separately, don't score as new lead

Verdict thresholds:

Score ≥ 75:
```
qualified
```
— Pursue actively
Score 50-74:
```
borderline
```
— Qualified with caveats, may need manual review
Score 30-49:
```
near_miss
```
— Not qualified now, but close enough to consider (referral or nurture)
Score < 30:
```
disqualified
```
— Does not fit ICP

Sub-verdicts for routing:

```
qualified_hot
```
— Score ≥ 75 AND Tier 1/2 urgency from triage
```
qualified_warm
```
— Score ≥ 75 AND Tier 3/4 urgency
```
borderline_review
```
— Score 50-74, needs human judgment call
```
near_miss_referral
```
— Score 30-49 AND right_company_wrong_person (referral opportunity)
```
near_miss_nurture
```
— Score 30-49, might fit in the future
```
disqualified_polite
```
— Score < 30, needs polite decline
```
disqualified_competitor
```
— Competitor employee
```
existing_customer_upsell
```
— Existing customer with expansion signal

将所有维度整合为最终的资格审核判定。

综合评分计算：

维度	权重	可选值
公司规模	15%	match=100, borderline=50, mismatch=0, unknown=30
行业	20%	match=100, adjacent=60, mismatch=0, unknown=30
公司阶段	10%	match=100, borderline=50, mismatch=0, unknown=30
地域	10%	match=100, borderline=50, mismatch=0, unknown=30
用例匹配度	25%	strong=100, moderate=60, weak=20, no_fit=0, unknown=30
联系人职位/角色	15%	exact=100, close=75, adjacent=40, mismatch=0, unknown=30
联系人职级	5%	match=100, too_junior=20, too_senior=60, unknown=30

硬性覆盖（绕过评分）：

存在任何硬性不合格项 → 无论评分如何均为
```
disqualified
```
存在任何硬性合格项 → 无论评分如何均为
```
qualified
```
（但仍需显示完整评分 breakdown）
现有客户 → 单独路由，不作为新线索评分

判定阈值：

评分≥75：
```
qualified
```
— 积极跟进
评分50-74：
```
borderline
```
— 合格但有附加条件，可能需要人工审核
评分30-49：
```
near_miss
```
— 当前不合格，但接近标准（可转介绍或培育）
评分<30：
```
disqualified
```
— 不符合ICP

用于路由的子判定：

```
qualified_hot
```
— 评分≥75 且分流时为1/2级紧急度
```
qualified_warm
```
— 评分≥75 且分流时为3/4级紧急度
```
borderline_review
```
— 评分50-74，需要人工判断
```
near_miss_referral
```
— 评分30-49 且标记为right_company_wrong_person（转介绍机会）
```
near_miss_nurture
```
— 评分30-49，未来可能符合标准
```
disqualified_polite
```
— 评分<30，需礼貌拒绝
```
disqualified_competitor
```
— 竞争对手员工
```
existing_customer_upsell
```
— 现有客户且有拓展信号

Output

输出

Each lead gets:

{
  "composite_score": 0-100,
  "verdict": "",
  "sub_verdict": "",
  "top_qualification_reasons": [],
  "top_disqualification_reasons": [],
  "summary": "One sentence: why this lead is/isn't a fit"
}

每条线索将生成：

{
  "composite_score": 0-100,
  "verdict": "",
  "sub_verdict": "",
  "top_qualification_reasons": [],
  "top_disqualification_reasons": [],
  "summary": "一句话说明：该线索为何匹配/不匹配"
}

Step 7: Output CSV

步骤7：输出CSV

CSV Structure

CSV结构

Produce a CSV with ALL input fields preserved plus qualification columns appended:

Core qualification columns:

```
qualification_verdict
```
— qualified | borderline | near_miss | disqualified
```
qualification_sub_verdict
```
— qualified_hot | qualified_warm | borderline_review | near_miss_referral | near_miss_nurture | disqualified_polite | disqualified_competitor | existing_customer_upsell
```
composite_score
```
— 0-100
```
summary
```
— One sentence qualification reasoning

Pipeline check columns:

```
pipeline_status
```
— new | existing_customer | in_pipeline | previously_contacted
```
pipeline_detail
```
— One sentence on the overlap
```
signal_flags
```
— Any signal composite matches

Company qualification columns:

```
company_size_score
```
— match | borderline | mismatch | unknown
```
industry_score
```
— match | adjacent | mismatch | unknown
```
stage_score
```
— match | borderline | mismatch | unknown
```
geography_score
```
— match | borderline | mismatch | unknown
```
use_case_score
```
— strong | moderate | weak | no_fit | unknown

Person qualification columns:

```
title_match_score
```
— exact_match | close_match | adjacent | mismatch | unknown
```
seniority_score
```
— match | too_junior | too_senior | unknown
```
authority_type
```
— economic_buyer | champion | user | evaluator | gatekeeper | unknown
```
mismatch_type
```
— null | right_company_wrong_person | right_person_wrong_company

Use case columns:

```
inferred_intent
```
— What they seem to need
```
product_fit
```
— strong | moderate | weak | unknown
```
implementation_feasibility
```
— easy | moderate | complex | unlikely

生成包含所有输入字段的CSV，并附加资格审核列：

核心资格审核列：

```
qualification_verdict
```
— qualified | borderline | near_miss | disqualified
```
qualification_sub_verdict
```
— qualified_hot | qualified_warm | borderline_review | near_miss_referral | near_miss_nurture | disqualified_polite | disqualified_competitor | existing_customer_upsell
```
composite_score
```
— 0-100
```
summary
```
— 一句话资格审核理由

销售管道检查列：

```
pipeline_status
```
— new | existing_customer | in_pipeline | previously_contacted
```
pipeline_detail
```
— 说明重叠情况的一句话（无则为null）
```
signal_flags
```
— 所有信号组合工具的匹配项

公司资格审核列：

```
company_size_score
```
— match | borderline | mismatch | unknown
```
industry_score
```
— match | adjacent | mismatch | unknown
```
stage_score
```
— match | borderline | mismatch | unknown
```
geography_score
```
— match | borderline | mismatch | unknown
```
use_case_score
```
— strong | moderate | weak | no_fit | unknown

联系人资格审核列：

```
title_match_score
```
— exact_match | close_match | adjacent | mismatch | unknown
```
seniority_score
```
— match | too_junior | too_senior | unknown
```
authority_type
```
— economic_buyer | champion | user | evaluator | gatekeeper | unknown
```
mismatch_type
```
— null | right_company_wrong_person | right_person_wrong_company

用例列：

```
inferred_intent
```
— 他们的潜在需求
```
product_fit
```
— strong | moderate | weak | unknown
```
implementation_feasibility
```
— easy | moderate | complex | unlikely

Save Location

保存位置

clients/<client-name>/leads/inbound-qualified-[date].csv

clients/<client-name>/leads/inbound-qualified-[date].csv

Summary Report

总结报告

After producing the CSV, present a summary:

markdown

undefined

生成CSV后，展示总结：

markdown

undefined

Inbound Lead Qualification: [Period]

入站线索资格审核：[时间段]

Total leads processed: X Qualified: X (Y%) — X hot, X warm Borderline (manual review): X (Y%) Near miss: X (Y%) — X referral opportunities, X nurture Disqualified: X (Y%)

Pipeline overlaps:

Existing customers: X (route to CS)
Already in pipeline: X (coordinate with deal owner)
Previously contacted: X (now warmer — re-engage)

Top qualification reasons:

[reason] — X leads
[reason] — X leads

Top disqualification reasons:

[reason] — X leads
[reason] — X leads

Data quality:

Leads with full data: X
Leads with partial data (some dimensions scored as 'unknown'): X
Leads needing enrichment: X

CSV saved to: [path]

---

处理的线索总数： X 合格： X（Y%）—— X条高意向，X条中意向 需人工审核的边缘线索： X（Y%） 接近合格： X（Y%）—— X条转介绍机会，X条需培育 不合格： X（Y%）

销售管道重叠情况：

现有客户：X（路由至客户成功团队）
已在销售管道：X（与交易负责人协调）
曾联系：X（现在意向更高——重新跟进）

主要合格理由：

[理由] — X条线索
[理由] — X条线索

主要不合格理由：

[理由] — X条线索
[理由] — X条线索

数据质量：

数据完整的线索：X
数据部分缺失（部分维度评分为'unknown'）的线索：X
需要补全数据的线索：X

CSV保存路径： [路径]

---

Handling Edge Cases

边缘情况处理

Lead with only an email (no name, no company):

Extract company domain from email
If corporate domain: look up the company, proceed with company qualification (person qualification will be mostly "unknown")
If personal email (gmail, yahoo): Score as
```
insufficient_data
```
, recommend enrichment or manual review

Same company, multiple leads:

Qualify the company once, apply to all leads from that company
Person qualification runs individually for each
Flag the multi-contact opportunity: "3 people from [Company] came inbound — potential committee buy"

Contradictory signals:

Company is strong fit but person is completely wrong (e.g., intern at a perfect company)
Score honestly. The sub-verdict
```
right_company_wrong_person
```
routes this to referral handling in
```
disqualification-handling
```

Borderline calls:

When the score is 50-74 and could go either way, lean toward qualifying for inbound leads
Rationale: they came to YOU. The intent signal tips borderline cases toward "worth a conversation"
Note this lean in the reasoning: "Borderline on [dimension], but inbound intent suggests pursuing"

Scoring with missing data:

Unknown dimensions score at 30 (not 0, not 50) — absence of data is mildly negative but not disqualifying
If >3 dimensions are "unknown", the lead is
```
insufficient_data
```
regardless of score — recommend enrichment first

仅提供邮箱的线索（无姓名、无公司）：

从邮箱提取公司域名
若为企业域名：查询公司信息，进行公司资格审核（联系人资格审核大部分为"unknown"）
若为个人邮箱（gmail、yahoo等）：评分为
```
insufficient_data
```
，建议补全数据或人工审核

同一公司的多条线索：

仅对公司进行一次资格审核，结果应用于该公司的所有线索
对每位联系人单独进行资格审核
标记多联系人机会：“[公司]有3位联系人发起入站请求——可能存在委员会采购”

矛盾信号：

公司匹配度高但联系人完全不匹配（例如：完美公司的实习生）
如实评分。子判定
```
right_company_wrong_person
```
将路由至
```
disqualification-handling
```
进行转介绍处理

边缘判定：

当评分在50-74之间且结果不确定时，入站线索倾向于判定为合格
理由：他们主动联系您，意向信号使边缘案例更值得“进行对话”
在理由中注明此倾向：“[维度]为边缘情况，但入站意向表明值得跟进”

缺失数据的评分：

未知维度评分为30（而非0或50）——数据缺失轻度负面但不导致不合格
若超过3个维度为“unknown”，无论评分如何，线索均为
```
insufficient_data
```
——建议先补全数据

Tools Required

所需工具

CRM access — to check pipeline, existing customers, outreach history
Supabase client — for pipeline/signal lookups
Web search — for company research when enrichment data is sparse
Enrichment tools — Apollo, LinkedIn scraper, or similar (optional, enhances accuracy)
Read/Write — for CSV I/O and config management

CRM访问权限 — 用于检查销售管道、现有客户、外呼历史
Supabase客户端 — 用于销售管道/信号查询
网络搜索 — 当补全数据不足时用于公司调研
数据补全工具 — Apollo、LinkedIn爬虫或类似工具（可选，提升准确性）
读写权限 — 用于CSV输入输出和配置管理