champion-tracker

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Champion Tracker

产品拥护者跟踪工具

Detect when product champions change jobs and qualify their new companies against ICP.

检测产品拥护者的工作变动情况，并根据理想客户画像（ICP）评估其新公司是否符合要求。

When to Use

适用场景

You have a list of known product users/champions (from reviews, LinkedIn posts, CRM exports)
You want to detect when they change companies (high-intent re-sell signal)
You want each job change scored against ICP before reaching out

你拥有已知的产品用户/拥护者列表（来自评论、LinkedIn帖子、CRM导出数据）
你希望检测他们是否跳槽（这是高意向的二次销售信号）
你希望在联系他们之前，根据ICP对每次跳槽进行评分

Two Phases

两个阶段

Phase A: Discover Champions (agent-driven, one-time)

阶段A：发现拥护者（由Agent驱动，一次性操作）

Build the initial champion list from public sources. This is done by the agent, not the script.

Scrape reviews — Use
```
review-scraper
```
skill to pull G2/Trustpilot reviews. Extract reviewer names + companies.
Search LinkedIn posts — Use Crustdata MCP to find people who posted about the product.
Resolve LinkedIn URLs — Use Crustdata MCP to search by name + company → get profile URLs.
Compile CSV — Merge all sources into
```
champions.csv
```
with required columns.

从公开数据源构建初始拥护者列表。此步骤由Agent完成，而非脚本。

抓取评论 — 使用
```
review-scraper
```
工具抓取G2/Trustpilot平台的评论，提取评论者姓名及所属公司。
搜索LinkedIn帖子 — 通过Crustdata MCP查找发布过相关产品内容的用户。
获取LinkedIn链接 — 通过Crustdata MCP根据姓名+公司信息搜索，获取用户的个人主页链接。
编译CSV文件 — 将所有来源的数据合并为包含必填列的
```
champions.csv
```
文件。

Phase B: Track Job Changes (script-driven, repeatable)

阶段B：跟踪工作变动（由脚本驱动，可重复执行）

Use

champion_tracker.py

for ongoing tracking.

使用

champion_tracker.py

脚本进行持续跟踪。

Script Usage

脚本使用说明

Prerequisites

前置条件

```
APIFY_API_TOKEN
```
in
```
.env
```
(for LinkedIn profile enrichment)

Champion CSV with columns:

name

linkedin_url

(required);

original_company

original_title

email

source

notes

(optional)

在
```
.env
```
文件中配置
```
APIFY_API_TOKEN
```
（用于LinkedIn个人资料数据富集）
拥护者CSV文件需包含以下列：
```
name
```
、
```
linkedin_url
```
（必填）；
```
original_company
```
、
```
original_title
```
、
```
email
```
、
```
source
```
、
```
notes
```
（可选）

Commands

命令说明

Initialize baseline (first run):

bash

undefined

初始化基准数据（首次运行）：

bash

undefined

Dry run — see cost estimate

试运行 — 查看成本估算

python3 skills/champion-tracker/scripts/champion_tracker.py init -i champions.csv --dry-run

Create baseline

创建基准快照

python3 skills/champion-tracker/scripts/champion_tracker.py init -i champions.csv


**Check for job changes** (subsequent runs):
```bash

python3 skills/champion-tracker/scripts/champion_tracker.py init -i champions.csv


**检查工作变动**（后续运行）：
```bash

Dry run

试运行

python3 skills/champion-tracker/scripts/champion_tracker.py check --dry-run

Detect changes and output CSV

检测变动并输出CSV文件

python3 skills/champion-tracker/scripts/champion_tracker.py check -o changes.csv


**View status**:
```bash
python3 skills/champion-tracker/scripts/champion_tracker.py status

python3 skills/champion-tracker/scripts/champion_tracker.py check -o changes.csv


**查看状态**：
```bash
python3 skills/champion-tracker/scripts/champion_tracker.py status

Output CSV Columns

输出CSV列说明

Column	Description
champion_name	Full name
linkedin_url	LinkedIn profile URL
previous_company	Company at baseline
previous_title	Title at baseline
new_company	Current company (changed)
new_title	Current title
change_detected_date	Date this check was run
position_start_date	When they started the new role
days_since_change	Days since new position started
icp_score	0-4 ICP qualification score
icp_verdict	Strong Fit / Good Fit / Possible Fit / Weak Fit
icp_notes	Scoring breakdown
email	Email if available
notes	Original notes from champion CSV

列名	描述
champion_name	全名
linkedin_url	LinkedIn个人主页链接
previous_company	基准快照中的原公司
previous_title	基准快照中的原职位
new_company	当前所在的新公司（已变动）
new_title	当前职位
change_detected_date	本次检测的执行日期
position_start_date	新职位的入职日期
days_since_change	入职新职位的天数
icp_score	0-4分的ICP匹配度评分
icp_verdict	强匹配/良好匹配/可能匹配/弱匹配
icp_notes	评分明细
email	（若有）邮箱地址
notes	来自拥护者CSV文件的原始备注

ICP Scoring (0-4)

ICP评分标准（0-4分）

Signal	Points	What it checks
B2B signal	1.0	Title contains sales/SDR/revenue/growth keywords
Outbound motion	1.0	Sales leadership title (VP Sales, Head of Growth, etc.)
Company size	1.0 / 0.5	SMB/mid-market = 1.0; unknown = 0.5 benefit-of-doubt
Seniority	1.0	VP, Director, Head of, C-level, Founder

Verdicts: Strong Fit (>=3) / Good Fit (>=2) / Possible Fit (>=1.5) / Weak Fit (<1.5)

评分项	分值	检测内容
B2B信号	1.0	职位名称包含销售/SDR/营收/增长等关键词
outbound拓展潜力	1.0	销售领导层职位（如销售副总裁、增长负责人等）
公司规模	1.0 / 0.5	中小企业/中型市场=1.0；规模未知=0.5（给予容错分）
职位层级	1.0	副总裁、总监、负责人、高管、创始人层级

判定标准：强匹配（≥3分）/良好匹配（≥2分）/可能匹配（≥1.5分）/弱匹配（<1.5分）

Cost

成本说明

~$3 per 1,000 LinkedIn profiles enriched
50-80 champions ≈ $0.15-0.25 per run
```
--dry-run
```
always shows cost before any API calls

每富集1000个LinkedIn个人资料约花费3美元
50-80名拥护者，每次运行约花费0.15-0.25美元
```
--dry-run
```
命令会在调用API前显示预估成本

File Structure

文件结构

skills/champion-tracker/
  SKILL.md                    # This file
  scripts/
    champion_tracker.py       # Main CLI script
  input/
    champions_template.csv    # Template for manual additions
  snapshots/                  # Created at runtime
    baseline.json             # Latest full snapshot
    archive/                  # Timestamped copies
  output/                     # Created at runtime
    changes-YYYY-MM-DD.csv    # Generated output

skills/champion-tracker/
  SKILL.md                    # 本文档
  scripts/
    champion_tracker.py       # 主CLI脚本
  input/
    champions_template.csv    # 手动添加数据的模板文件
  snapshots/                  # 运行时自动创建
    baseline.json             # 最新的完整快照
    archive/                  # 带时间戳的历史快照副本
  output/                     # 运行时自动创建
    changes-YYYY-MM-DD.csv    # 生成的输出文件

Dependencies

依赖项

Reuses

LinkedInEnricher

from

skills/lead-qualification/scripts/enrich_leads.py

Falls back to inline implementation if import fails
Requires:
```
requests
```
(Python package),
```
APIFY_API_TOKEN
```
(env var)

复用

skills/lead-qualification/scripts/enrich_leads.py

中的

LinkedInEnricher

模块

若导入失败，则使用内置实现
依赖：
```
requests
```
（Python包）、
```
APIFY_API_TOKEN
```
（环境变量）