Verwendung
- Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
- Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
- Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-real-estate-professionals-on-twitterVor dem Start
- Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
- Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument
Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.
Finding Real Estate Professionals On Twitter
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 |
How to Run
Using run_actor.js (recommended)
bash
# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'
# Save as CSV
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.csv --format csv
# Save as JSON
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.json --format jsonREST API fallback
bash
curl -X POST "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'If Apify MCP is available: Use the Apify MCP call_actor tool with actor apidojo~twitter-user-scraper and the input above.
Scoring & Ranking
Score each user by:
followers→ normalized 0-1 (cap at 50K), weight 0.30bio_match_score(contains: realtor, broker, real estate, property, agent, MLS) → 0 or 1, weight 0.40has_website→ 0 or 1, weight 0.30
python
score = 0.30 * min(followers / 50000, 1.0) + 0.40 * int(bio_match) + 0.30 * int(has_website)Classification
| Score | Tier | Label |
|---|---|---|
| ≥ 0.70 | A | PRIME_OUTREACH |
| 0.40–0.69 | B | HOT_CANDIDATE |
| < 0.40 | C | LOW_PRIORITY |
Edge Cases
- Generic bio keywords: "house" or "home" match too broadly. Use "realtor", "real estate agent", "MLS".
- Personal accounts mixed in: Filter by followers > 200 and has website link.
- Bot accounts: Unusually high following-to-follower ratio — filter out.
- Keyword not in bio: Twitter user search matches bio text — results may vary if bio is non-standard.
- International agents: Use country-specific terms (e.g., "estate agent" for UK).
