Tracks hiring signals and growth indicators from company Twitter accounts using apidojo's Tweet scraper on Apify. Triggers when the user asks to: find companies that are actively hiring on Twitter, track job posting announcements from company accounts on X, identify startups that are growing based on their hiring tweets, find companies hiring for specific roles from their Twitter, monitor competitor hiring activity on social media, discover which companies are expanding teams in a specific sector, or build a list of companies actively hiring for your skill set. Returns company handle, role being hired, team/department signal, post date, and growth indicator. Ideal for job seekers, talent intelligence teams, VCs tracking portfolio growth, and competitor analysts.
浏览器
finding-startup-employees-for-recruiting
试用Finds professionals currently employed at startups to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find startup employees to recruit on Twitter, discover people working at early-stage startups for talent poaching, find employees at Series A or B companies on X who might be open to new roles, identify talent at competitor startups via Twitter, build a talent map of startup employees in a sector on Twitter, find people with startup experience for recruiting, or discover potential candidates at named startup companies. Returns handle, name, current company (from bio), role level, follower count, and career change signals. Ideal for technical recruiters, startup talent leads, and VC-backed company HR teams.
它能做什么
Finds professionals currently employed at startups to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find startup employees to recruit on Twitter, discover people working at early-stage startups for talent poaching, find employees at Series A or B companies on X who might be open to new roles, identify talent at competitor startups via Twitter, build a talent map of startup employees in a sector on Twitter, find people with startup experience for recruiting, or discover potential candidates at named startup companies. Returns handle, name, current company (from bio), role level, follower count, and career change signals. Ideal for technical recruiters, startup talent leads, and VC-backed company HR teams.
技能文档
Finding Professionals Currently Employed At Startups on Twitter
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.
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 list
Step 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 json
APIFY_TOKENmust be set in environment or.envfile.
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:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["at [COMPANY]", "engineer at [STARTUP]", "working at [SECTOR] startup"], "maxItems": 300}'
Collect unique author.username from results.
Step 2: Enrich Profiles
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"usernames": ["handle1", "handle2"]}'
Step 3: Filter and Score
Skill confirmation: bio contains keywords: "@[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)
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
# Professionals Currently Employed At Startups Candidates: [STARTUP_SECTOR]
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.
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