finding-software-engineers-on-twitter

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English

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Chinese

Finding Software Engineers on Twitter

在Twitter上寻找软件工程师

Discovers software engineers on Twitter/X via tech stack keywords, open-to-work signals, and engineering community activity. Twitter surfaces engineers who are active in their tech community — a strong passive candidate signal.

通过技术栈关键词、求职意向信号以及技术社区活跃度在Twitter/X上发掘软件工程师。Twitter会展示活跃于技术社区的工程师——这是一个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

已设置
```
APIFY_TOKEN
```
环境变量
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主页或推文URL
`twitterHandles`	array	可选	`[]`	Twitter用户名（不带@）
`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`	提取推文URL的转发者
`includeUnavailableUsers`	boolean	可选	`false`	包含不可用/被封禁的用户
`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 tweets by tech stack + role signals
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles via twitter-user-scraper
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list

进度:
- [ ] 步骤1：按技术栈+角色信号搜索推文
- [ ] 步骤2：收集唯一账号昵称
- [ ] 步骤3：通过twitter-user-scraper丰富个人资料
- [ ] 步骤4：对候选人匹配度打分
- [ ] 步骤5：交付候选人列表

Step 1: Search Queries

步骤1：搜索查询语句

Queries: ["[TECH_STACK] engineer", "[TECH_STACK] developer", "senior [TECH_STACK]",
          "built with [TECH_STACK]", "[TECH_STACK] open to work", "[TECH_STACK] job search"]

For open-to-work pass: add

"looking for [TECH_STACK] role"

"[TECH_STACK] available"

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

bash

undefined

查询语句: ["[TECH_STACK] engineer", "[TECH_STACK] developer", "senior [TECH_STACK]",
          "built with [TECH_STACK]", "[TECH_STACK] open to work", "[TECH_STACK] job search"]

针对求职意向筛选：添加

"looking for [TECH_STACK] role"

、

"[TECH_STACK] available"

推荐方案 — run_actor.js（自动处理等待、输出和文件保存）：

bash

undefined

Quick answer (prints table to chat)

快速输出（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格式

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格式

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": ["[TECH_STACK] engineer", "senior [TECH_STACK] developer", "built with [TECH_STACK]"], "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": ["Python engineer", "senior Python developer", "built with Python"], "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`。

**若Apify MCP可用：**

工具: apify:run-actor Actor: "apidojo~tweet-scraper" 输入: { "searchTerms": ["[TECH_STACK] engineer", "senior [TECH_STACK] developer", "built with [TECH_STACK]"], "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": ["Python engineer", "senior Python developer", "built with Python"], "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"]}'

若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: Score Candidate Quality

步骤3：候选人质量打分

Tech stack confirmation: bio or recent tweets mention the target tech stack →

stack_confirmed = true

Role level proxy from bio:

"senior", "staff", "principal", "lead", "CTO", "VP Eng" → senior+
"mid", "3+ years", "5 years" → mid
"junior", "new grad", "bootcamp" → junior

Open-to-work score:

candidate_score = (stack_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

Active = tweeted in last 30 days; Passive = 30–90 days; Dormant = > 90 days

**技术栈确认：**个人简介或近期推文提及目标技术栈 →

stack_confirmed = true

通过个人简介判断角色级别：

"senior"、"staff"、"principal"、"lead"、"CTO"、"VP Eng" → 资深及以上
"mid"、"3+ years"、"5 years" → 中级
"junior"、"new grad"、"bootcamp" → 初级

求职意向得分：

candidate_score = (stack_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

活跃状态 = 近30天发布过推文；被动状态 = 30-90天内发布过推文；休眠状态 = 超过90天未发布推文

Step 4: Edge Cases

步骤4：特殊情况处理

Results dominated by developer tools companies: Filter out accounts where
```
followerCount > 50K
```
and bio mentions company/brand — these are likely dev tool marketing accounts
Location filtering: Twitter bio location is free text — use
```
contains
```
match; filter out profiles with ambiguous or non-geographic location entries
< 20 results for niche stack: Broaden to language family (e.g. "Rust" → "systems programming") or remove role level filter
Bot accounts: Flag profiles where
```
follower/following ratio < 0.05
```
AND
```
tweetsCount < 10
```
as likely bots

结果多为开发者工具公司账号：过滤掉
```
followerCount > 50K
```
且个人简介提及公司/品牌的账号——这些很可能是开发者工具的营销账号
地理位置过滤：Twitter个人简介中的地理位置为自由文本——使用
```
contains
```
匹配；过滤掉地理位置模糊或非地理相关的个人资料
小众技术栈结果不足20条：扩大搜索范围至语言家族（例如："Rust" → "systems programming"）或移除角色级别过滤条件
机器人账号：标记
```
follower/following ratio < 0.05
```
且
```
tweetsCount < 10
```
的账号为疑似机器人

Output Format

输出格式

undefined

undefined

Software Engineer Candidates: [TECH_STACK]

软件工程师候选人：[TECH_STACK]

找到的个人资料数量：[N] | 有求职意向：[N] | 资深：[N] | 中级：[N] | 活跃：[N] | 日期：[DATE]

Priority: Open-to-Work Candidates

优先级：有求职意向的候选人

Name	@Handle	Role Level	Location	Stack Confirmed	Followers	Last Active	Score
[name]	@[handle]	Senior	[city]	✓	[N]	[X days ago]	[0.XX]

姓名	@账号昵称	角色级别	地理位置	技术栈已确认	粉丝数	最后活跃时间	得分
[name]	@[handle]	Senior	[city]	✓	[N]	[X天前]	[0.XX]

Passive Candidates (Not Actively Searching)

被动候选人（未主动求职）

Name	@Handle	Role Level	Location	Stack	Followers	Score

姓名	@账号昵称	角色级别	地理位置	技术栈	粉丝数	得分

Bio Highlights

个人简介亮点

Top 5 candidates — summarized bios:

@[handle]: "[bio excerpt]" — [tech signals]

undefined

排名前5的候选人——简介摘要：

@[handle]: "[简介片段]" — [技术信号]

undefined

Troubleshooting

故障排除

Results are all companies not individuals: Add

"-company -official -team -agency"

as negative search terms, or filter bio for first-person pronouns. Tech stack too common returns too many results: Add a second filter — location OR seniority level — to reduce to a manageable size. Few open-to-work signals: Most passive candidates don't signal openly; focus outreach on the

passive

tier with personalized messages referencing their recent tweets.

结果全是公司账号而非个人账号：添加

"-company -official -team -agency"

作为负面搜索词，或过滤包含第一人称代词的个人简介。 技术栈过于通用导致结果过多：添加第二个过滤条件——地理位置或资历级别——以将结果缩减至可处理的数量。 求职意向信号极少：大多数被动候选人不会公开表达求职意向；重点联系

passive

层级的候选人，并发送提及他们近期推文的个性化消息。