finding-startup-employees-for-recruiting — скилл для Claude Code

---
name: finding-startup-employees-for-recruiting
description: >
  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.
license: Apache-2.0
metadata:
  author: apidojo
  version: "1.0"
  apify-actors: apidojo/twitter-user-scraper, apidojo/tweet-scraper
---

# 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.