finding-startup-employees-for-recruiting

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English

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Chinese

Finding Professionals Currently Employed At Startups on Twitter

在Twitter上寻找目前受雇于初创公司的专业人士

Discovers professionals currently employed at startups on Twitter via skill keywords, portfolio/project signals, and open-to-work indicators. Twitter surfaces professionals who actively discuss their craft — a strong passive candidate signal.

通过技能关键词、作品集/项目信号以及“开放求职”标识，在Twitter上发掘目前受雇于初创公司的专业人士。Twitter会展示那些积极讨论自身专业领域的专业人士——这是很强的被动候选人信号。

Prerequisites

前提条件

```
APIFY_TOKEN
```
environment variable set
Optional: Apify MCP server installed

```
APIFY_TOKEN
```
环境变量已设置
可选：已安装Apify MCP服务器

Inputs

输入参数

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile or tweet URLs
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`twitterUserIds`	array	Optional	`[]`	Twitter user IDs
`getFollowers`	boolean	Optional	`false`	Extract follower lists
`getFollowing`	boolean	Optional	`false`	Extract following lists
`getRetweeters`	boolean	Optional	`false`	Extract retweeters of a tweet URL
`includeUnavailableUsers`	boolean	Optional	`false`	Include unavailable/suspended users
`maxItems`	number	Optional	Unlimited	Maximum users to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

参数	类型	是否必填	默认值	说明
`startUrls`	array	可选	`[]`	Twitter个人主页或推文URL
`twitterHandles`	array	可选	`[]`	Twitter用户名（不含@）
`twitterUserIds`	array	可选	`[]`	Twitter用户ID
`getFollowers`	boolean	可选	`false`	提取粉丝列表
`getFollowing`	boolean	可选	`false`	提取关注列表
`getRetweeters`	boolean	可选	`false`	提取某条推文的转发者
`includeUnavailableUsers`	boolean	可选	`false`	包含无法访问/被暂停的用户
`maxItems`	number	可选	无限制	返回的最大用户数量
`customMapFunction`	string	可选	—	用于转换每个输出对象的JavaScript函数

Workflow

工作流程

Progress:
- [ ] Step 1: Search for role-specific tweets
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list

进度：
- [ ] 步骤1：搜索特定职位的推文
- [ ] 步骤2：收集唯一账号昵称
- [ ] 步骤3：丰富个人资料
- [ ] 步骤4：评估候选人匹配度
- [ ] 步骤5：交付候选人列表

Step 1: Search Queries

步骤1：搜索查询

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash

undefined

推荐方式 — 使用run_actor.js（自动处理等待、输出和文件保存）：

bash

undefined

Quick answer (prints table to chat)

快速查看（在聊天中打印表格）

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'

Save as CSV

保存为CSV文件

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv

Save as JSON

保存为JSON文件

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> `APIFY_TOKEN` must be set in environment or `.env` file.

**If Apify MCP is available:**

Tool: apify:run-actor Actor: "apidojo~tweet-scraper" Input: { "searchTerms": ["at [COMPANY]", "engineer at [STARTUP]", "working at [SECTOR] startup"], "maxItems": 300, "tweetLanguage": "en" }


**REST API fallback:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["at [COMPANY]", "engineer at [STARTUP]", "working at [SECTOR] startup"], "maxItems": 300}'

Collect unique

author.username

from results.

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> 必须在环境变量或`.env`文件中设置`APIFY_TOKEN`。

**如果Apify MCP可用：**

工具：apify:run-actor Actor: "apidojo~tweet-scraper" 输入： { "searchTerms": ["at [COMPANY]", "engineer at [STARTUP]", "working at [SECTOR] startup"], "maxItems": 300, "tweetLanguage": "en" }


**REST API备选方案：**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["at [COMPANY]", "engineer at [STARTUP]", "working at [SECTOR] startup"], "maxItems": 300}'

从结果中收集唯一的

author.username

。

Step 2: Enrich Profiles

步骤2：丰富个人资料

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}

REST API fallback:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

如果Apify MCP可用：

工具：apify:run-actor
Actor: "apidojo~twitter-user-scraper"
输入: {"usernames": ["[username1]", "[username2]", "..."]}

REST API备选方案：

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

Step 3: Filter and Score

步骤3：筛选与评分

Skill confirmation: bio contains keywords: "@[company]", "prev:", "formerly", "ex-", "startup", "Series A", "YC", "Techstars"

career_change_signal = bio or recent tweets mention 'open to', 'looking for', 'next chapter', or company departure

Candidate score:

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)

技能确认： 个人简介包含以下关键词："@[company]", "prev:", "formerly", "ex-", "startup", "Series A", "YC", "Techstars"

职业变动信号 = 个人简介或近期推文提及'open to', 'looking for', 'next chapter'或离职相关内容

候选人评分：

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

活跃度：活跃（<30天）| 被动（30–90天）| 沉寂（>90天）

Step 4: Edge Cases

步骤4：边缘情况处理

Company/brand accounts in results: Filter where
```
followerCount > 50K
```
AND bio contains no personal pronouns; these are likely brand accounts
< 20 candidates found: Broaden skill term; remove location or seniority filter; try adjacent skills

Bot detection: Flag

followerCount / followingCount < 0.05

AND

tweetsCount < 20

as potential bot

Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names

结果中包含企业/品牌账号： 筛选掉
```
followerCount > 50K
```
且个人简介中无第一人称代词的账号；这些很可能是品牌账号
找到的候选人不足20名： 放宽技能术语限制；移除地点或资历筛选；尝试相关技能关键词
机器人检测： 将
```
followerCount / followingCount < 0.05
```
且
```
tweetsCount < 20
```
的账号标记为潜在机器人
地点不匹配： 个人简介中的地点为自由文本——使用模糊匹配；接受部分城市/国家名称

Output Format

输出格式

undefined

undefined

Professionals Currently Employed At Startups Candidates: [STARTUP_SECTOR]

初创公司在职专业人士候选人：[STARTUP_SECTOR]

Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]

找到的资料数量：[N] | 开放求职：[N] | 活跃状态：[N] | 日期：[DATE]

Priority: Open-to-Work Candidates

优先级：开放求职候选人

Name	@Handle	Specialty	Location	Followers	Last Active	Score

姓名	@账号昵称	专业领域	地点	粉丝数	最后活跃时间	评分

Passive Candidates

被动候选人

Name	@Handle	Specialty	Location	Followers	Score

姓名	@账号昵称	专业领域	地点	粉丝数	评分

Bio Highlights (Top 5)

个人简介亮点（前5名）

@[handle]: "[bio excerpt]"

undefined

@[handle]: "[个人简介节选]"

undefined

Troubleshooting

故障排除

All results are agencies/companies not individuals: Add personal pronouns filter or search

"I am a [role]"

"I do [skill]"

. Role too generic returns too many results: Add location OR seniority qualifier. No open-to-work signals: Most candidates don't signal publicly — treat passive candidates as warm leads with personalized outreach referencing their recent content.

所有结果均为代理机构/企业而非个人： 添加第一人称代词筛选，或搜索

"I am a [role]"

"I do [skill]"

。 职位过于宽泛导致结果过多： 添加地点或资历限定词。 无开放求职信号： 大多数候选人不会公开发出此类信号——将被动候选人视为潜在意向人选，结合其近期内容进行个性化沟通。