bosszp
BOSS直聘岗位数据爬虫 + 可视化分析工具。使用 Scrapy 爬取职位数据，存入 MySQL，用 Flask + Highcharts 生成可视化报告。当用户需要批量采集 BOSS直聘 职位数据、分析薪资分布、公司分布、岗位要求，或需要生成可视化报告时触发。
Best use case
bosszp is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
BOSS直聘岗位数据爬虫 + 可视化分析工具。使用 Scrapy 爬取职位数据，存入 MySQL，用 Flask + Highcharts 生成可视化报告。当用户需要批量采集 BOSS直聘 职位数据、分析薪资分布、公司分布、岗位要求，或需要生成可视化报告时触发。
Teams using bosszp should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bosszp/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bosszp Compares
| Feature / Agent | bosszp | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
BOSS直聘岗位数据爬虫 + 可视化分析工具。使用 Scrapy 爬取职位数据，存入 MySQL，用 Flask + Highcharts 生成可视化报告。当用户需要批量采集 BOSS直聘 职位数据、分析薪资分布、公司分布、岗位要求，或需要生成可视化报告时触发。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# BOSS直聘 数据爬虫分析 (bosszp)
## 环境要求
- Python 3.x
- MySQL
- Scrapy
- Pandas + SQLAlchemy
- Flask + Highcharts
## 安装依赖
```bash
pip install scrapy
pip install flask pandas sqlalchemy pymysql
pip install flash
```
## 数据采集
### 1. 创建 Scrapy 项目
```bash
scrapy startproject bosszp
cd bosszp
scrapy genspider boss zhipin.com
```
### 2. 配置 Cookie 和请求头
编辑 `settings.py`：
```python
COOKIES_ENABLED = True
USER_AGENTS = [
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36...',
...
]
# 爬取延迟（避免被封）
DOWNLOAD_DELAY = 2
```
### 3. 运行爬虫
```bash
scrapy crawl boss -o output.csv
```
## 数据导出
### CSV 格式
岗位名称,岗位地区,薪资,公司名称,公司类型,公司规模,融资阶段,工作年限,学历,福利标签
### 导入 MySQL
```bash
python clean_data.py # 数据清洗 + 导入 MySQL
```
## 可视化看板
```bash
cd web
export FLASK_APP=run.py
flask run
# 访问 http://127.0.0.1:8080/
```
看板包含：
- **饼图**: 公司融资阶段分布
- **柱状图**: 薪资分布 Top10
- **词云**: 岗位福利标签词云
- **表格**: 公司排名 Top10（按岗位数量）
## 字段说明
| 字段 | 说明 |
|------|------|
| job_name | 岗位名称 |
| job_area | 工作地区 |
| job_salary | 薪资范围 |
| com_name | 公司名称 |
| com_type | 公司类型（国企/民营等） |
| com_size | 公司规模 |
| finance_stage | 融资阶段 |
| work_year | 工作年限要求 |
| education | 学历要求 |
| job_benefits | 福利标签 |Related Skills
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