--- 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):** ```bash # 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:** ```bash curl -X POST \ "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"searchTerms": ["[COMPETITOR] hiring", "[COMPETITOR] join our team", "[COMPETITOR] job opening"], "maxItems": 100}' ``` Wait for `SUCCEEDED`. Fetch dataset: ```bash curl "https://api.apify.com/v2/actor-runs/$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.