Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-real-estate-professionals-on-twitterAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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).
