monitoring-twitter-for-competitor-job-posts

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 Twitt…

API Dojo

@apidojo-io

Install

$ openclaw skills install @apidojo-io/monitoring-twitter-for-competitor-job-posts

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

ParameterTypeRequiredDefaultNotes
searchTermsarray[]Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"])
sortstringOptionalTopSort order: Latest, Top, or Latest+Top
tweetLanguagestringOptionalISO 639-1 language code (e.g. en)
maxItemsnumberOptionalUnlimitedMaximum tweets to return
onlyVerifiedUsersbooleanOptionalfalseOnly tweets from verified users
onlyTwitterBluebooleanOptionalfalseOnly Twitter Blue subscribers
onlyImagebooleanOptionalfalseOnly tweets with images
onlyVideobooleanOptionalfalseOnly tweets with videos
onlyQuotebooleanOptionalfalseOnly quote tweets
authorstringOptionalFilter to a specific author handle
inReplyTostringOptionalTweets replying to a specific handle
mentioningstringOptionalTweets mentioning a specific handle
geotaggedNearstringOptionalTweets near a location
withinRadiusstringOptionalRadius around geotaggedNear
geocodestringOptionalLat/lng + radius string
placeObjectIdstringOptionalTweets tagged with a place
minimumRetweetsnumberOptionalMinimum retweet count
minimumFavoritesnumberOptionalMinimum like count
minimumRepliesnumberOptionalMinimum reply count
startstringOptionalTweets after this date (YYYY-MM-DD)
endstringOptionalTweets before this date (YYYY-MM-DD)
includeSearchTermsbooleanOptionalfalseAdd the matched search term to each tweet
customMapFunctionstringOptionalJavaScript 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.

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