사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-data-scientists-on-twitter시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Finding Data Scientists And Ml Engineers on Twitter
Discovers data scientists and ML engineers 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.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| 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 |
Workflow
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 listStep 1: Search Queries
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"],
"maxItems": 300,
"tweetLanguage": "en"
}REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"], "maxItems": 300}'Collect unique author.username from results.
Step 2: Enrich Profiles
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"usernames": ["handle1", "handle2"]}'Step 3: Filter and Score
Skill confirmation: bio contains keywords: "data science", "machine learning", "ML", "NLP", "LLM", "AI", "neural network", "PyTorch", "TensorFlow", "scikit-learn"
research_signal = bio contains 'PhD', 'researcher', or links to papers/Google Scholar
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) * 0.15Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)
Step 4: Edge Cases
- Company/brand accounts in results: Filter where
followerCount > 50KAND 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 < 0.05ANDtweetsCount < 20as potential bot - Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names
Output Format
# Data Scientists And Ml Engineers Candidates: [ML_SPECIALTY]
Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]
## Priority: Open-to-Work Candidates
| Name | @Handle | Specialty | Location | Followers | Last Active | Score |
|------|---------|----------|---------|-----------|------------|-------|
## Passive Candidates
| Name | @Handle | Specialty | Location | Followers | Score |
|------|---------|----------|---------|-----------|-------|
## Bio Highlights (Top 5)
1. @[handle]: "[bio excerpt]"Troubleshooting
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.
