Metadata

name: monitoring-twitter-for-competitor-job-posts
description: Monitors Twitter for competitor hiring announcements to track growth signals using apidojo's Tweet scraper. Triggers when the user asks to: monitor competitor job postings on Twitter, track hiring signals from competitor companies on X, find out what roles competitors are hiring for on Twitter, analyze competitor team growth from their Twitter activity, monitor startup hiring signals for competitive intelligence, track which departments competitors are growing via their Twitter, or discover competitor expansion strategies from job post tweets. Returns company handle, role being posted, department, posting date, urgency signals, and growth pattern. Ideal for competitive intelligence teams, recruiters targeting competitor employees, and investors tracking company growth.
license: Apache-2.0
metadata: {"author":"apidojo","version":"1.0","apify-actor":"apidojo/tweet-scraper"}

Monitoring Twitter For Competitor Job Posts

Executes monitoring twitter for competitor job posts using apidojo scrapers. Part of the apidojo intelligence skills library.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

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

Workflow

Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver output

Step 2: Run the Actor

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_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[COMPETITOR] hiring", "[COMPETITOR] join our team", "[COMPETITOR] job opening"],
  "maxItems": 100
}

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": ["[COMPETITOR] hiring", "[COMPETITOR] join our team", "[COMPETITOR] job opening"], "maxItems": 100}'

Wait for SUCCEEDED. Fetch dataset:

curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"

Step 3: Classify Results

classification: AGGRESSIVE_GROWTH (> 5 roles/month) | STEADY_GROWTH (2-5 roles/month) | OPPORTUNISTIC (1-2 roles) | REPLACEMENT_ONLY (role title identical to recent departure signal)

Step 4: Score Each Result

score = growth_signal_score = (roles_posted_in_30_days / 5, max 1) * 0.50 + (engineering_role ? 1.2 : 1) * department_weight * 0.30 + (urgency_language ? 1 : 0.5) * 0.20

Step 5: Edge Cases

Competitor may post jobs across many channels but only announce key roles on Twitter — Twitter hiring posts often signal strategic priorities, not routine backfills

Additional fallbacks:

< 20 results: Broaden search terms; remove secondary filters
No results: Verify the search terms are correct; try alternate phrasings
Data quality issues: Remove entries with missing key fields; note count in output

Output Format

# Monitoring Twitter For Competitor Job Posts
Results: [N] | Date: [DATE]

| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |

## Summary
Top result: [description]
Key finding: [insight]

Troubleshooting

Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.

monitoring-twitter-for-competitor-job-posts

安装技能