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-designers-and-creatives-on-twitterVor dem Start
- Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
- Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.
Finding Designers And Creative Professionals on Twitter
Discovers designers and creative professionals on Twitter via skill keywords, portfolio/project signals, and open-to-work indicators. Twitter surfaces professionals who actively discuss their craft — a strong passive candidate signal.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
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 |
Workflow
Progress:
- [ ] Step 1: Search for role-specific tweets
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate listStep 1: Search Queries
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~twitter-user-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["UX designer", "product designer", "portfolio", "[DISCIPLINE] open to work"],
"maxItems": 300,
"tweetLanguage": "en"
}REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["UX designer", "product designer", "portfolio", "[DISCIPLINE] open to work"], "maxItems": 300}'Collect unique author.username from results.
Step 2: Enrich Profiles
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"usernames": ["handle1", "handle2"]}'Step 3: Filter and Score
Skill confirmation: bio contains keywords: "UX", "UI", "product design", "graphic design", "motion", "brand design", "Figma", "Sketch", "Adobe"
portfolio_link_signal = bio contains figma.com, behance.net, dribbble.com, or personal domain → strong talent signal
Candidate score:
candidate_score = (skill_confirmed ? 1 : 0) * 0.35
+ (open_to_work_signal ? 1 : 0) * 0.30
+ (followerCount in 200..20000 ? 1 : 0.6) * 0.20
+ (tweeted_in_last_30_days ? 1 : 0) * 0.15Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)
Step 4: Edge Cases
- Company/brand accounts in results: Filter where
followerCount > 50KAND bio contains no personal pronouns; these are likely brand accounts - < 20 candidates found: Broaden skill term; remove location or seniority filter; try adjacent skills
- Bot detection: Flag
followerCount / followingCount < 0.05ANDtweetsCount < 20as potential bot - Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names
Output Format
# Designers And Creative Professionals Candidates: [DESIGN_DISCIPLINE]
Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]
## Priority: Open-to-Work Candidates
| Name | @Handle | Specialty | Location | Followers | Last Active | Score |
|------|---------|----------|---------|-----------|------------|-------|
## Passive Candidates
| Name | @Handle | Specialty | Location | Followers | Score |
|------|---------|----------|---------|-----------|-------|
## Bio Highlights (Top 5)
1. @[handle]: "[bio excerpt]"Troubleshooting
All results are agencies/companies not individuals: Add personal pronouns filter or search "I am a [role]", "I do [skill]". Role too generic returns too many results: Add location OR seniority qualifier. No open-to-work signals: Most candidates don't signal publicly — treat passive candidates as warm leads with personalized outreach referencing their recent content.
