finding-startup-employees-for-recruiting

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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.

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Sourceapidojo-io/apidojo-skills

Added on2026-09-03

NPX Install

npx skill4agent add apidojo-io/apidojo-skills finding-startup-employees-for-recruiting

SKILL.md Content

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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_TOKEN
```
environment 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):

bash

# 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_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.

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:

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

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 > 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

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.

finding-startup-employees-for-recruiting

NPX Install

Tags

SKILL.md Content

Finding Professionals Currently Employed At Startups on Twitter

Prerequisites

Inputs

Workflow

Step 1: Search Queries

Step 2: Enrich Profiles

Step 3: Filter and Score

Step 4: Edge Cases

Output Format

Troubleshooting