Install
openclaw skills install @apidojo-io/tracking-hiring-signals-from-company-tweetsTracks 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.
openclaw skills install @apidojo-io/tracking-hiring-signals-from-company-tweetsMonitors company Twitter accounts for hiring announcements and growth signals. Companies tweet about openings before jobs.page or LinkedIn posts go live — Twitter is an early signal channel.
APIFY_TOKEN environment variable set| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
searchTerms | array | ✅ | [] | Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"]) |
sort | string | Optional | Top | Sort order: Latest, Top, or Latest+Top |
tweetLanguage | string | Optional | — | ISO 639-1 language code (e.g. en) |
maxItems | number | Optional | Unlimited | Maximum tweets to return |
onlyVerifiedUsers | boolean | Optional | false | Only tweets from verified users |
onlyTwitterBlue | boolean | Optional | false | Only Twitter Blue subscribers |
onlyImage | boolean | Optional | false | Only tweets with images |
onlyVideo | boolean | Optional | false | Only tweets with videos |
onlyQuote | boolean | Optional | false | Only quote tweets |
author | string | Optional | — | Filter to a specific author handle |
inReplyTo | string | Optional | — | Tweets replying to a specific handle |
mentioning | string | Optional | — | Tweets mentioning a specific handle |
geotaggedNear | string | Optional | — | Tweets near a location |
withinRadius | string | Optional | — | Radius around geotaggedNear |
geocode | string | Optional | — | Lat/lng + radius string |
placeObjectId | string | Optional | — | Tweets tagged with a place |
minimumRetweets | number | Optional | — | Minimum retweet count |
minimumFavorites | number | Optional | — | Minimum like count |
minimumReplies | number | Optional | — | Minimum reply count |
start | string | Optional | — | Tweets after this date (YYYY-MM-DD) |
end | string | Optional | — | Tweets before this date (YYYY-MM-DD) |
includeSearchTerms | boolean | Optional | false | Add the matched search term to each tweet |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Progress:
- [ ] Step 1: Search for hiring-signal tweets in sector
- [ ] Step 2: (Optional) Scrape specific company accounts
- [ ] Step 3: Extract role and team info from tweet text
- [ ] Step 4: Score growth signal strength
- [ ] Step 5: Deliver hiring intelligence report
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~tweet-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": ["we're hiring [SECTOR]", "join our team [SECTOR]", "[ROLE] hiring [SECTOR]"],
"maxItems": 300
}
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": ["we are hiring fintech", "join our team SaaS startup", "software engineer hiring"],
"maxItems": 300
}'
role_mentioned = extract noun phrases after "hiring a/an", "looking for a/an", "seeking a"
team_mentioned = extract from: "engineering team", "sales team", "marketing", "product team"
urgency = "immediately", "ASAP", "urgent" → HIGH; "growing team" → MEDIUM; general → LOW
hiring_signal_score = (is_confirmed_company_account ? 1 : 0.5) * 0.30
+ (role_is_specific ? 1 : 0.5) * 0.25
+ (post_is_recent: ≤7 days = 1, 8-14 = 0.7, 15-30 = 0.4) * 0.25
+ (link_to_job_page ? 1 : 0) * 0.20
Growth tier: Multiple hiring tweets in 30 days = HIGH_GROWTH; 1-2 = STEADY_HIRE; no link = SIGNAL_ONLY
role = "unspecified" — still a growth signalauthor.username; count unique companies, not unique tweets# Hiring Signal Intelligence: [SECTOR/ROLE]
Tweets analyzed: [N] | Companies with hiring signals: [N] | Date: [DATE]
## High-Growth Companies (Multiple Roles Posted)
| Company | @Handle | Roles Mentioned | Teams | Posts | Link to Jobs | Score |
|---------|---------|----------------|-------|-------|-------------|-------|
| [name] | @[handle] | [role list] | [eng/mktg] | [N] | [Yes/No] | [0.XX] |
## Single Hire Signals
| Company | @Handle | Role | Team | Tweet Date | Job Link |
|---------|---------|------|------|-----------|---------|
## Role Distribution Across All Companies
| Role Category | # Companies Hiring | Urgency |
|--------------|-------------------|---------|
| Engineering | [N] | [High/Med] |
| Marketing | [N] | |
| Sales | [N] | |
Results dominated by job boards: Add negative terms to search: -jobs -jobboard -staffing -recruiting
Sector too broad: Narrow with a sub-sector or specific stage: "Series A fintech" instead of "fintech".
Job links are broken/expired: Hiring tweets are posted in real-time; check within 48h for best link validity.