apidojo-io/apidojo-skills

finding-speaking-opportunities-on-twitter

Finds speaking opportunities and event organizer contacts on Twitter using apidojo's Twitter scrapers.

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사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-speaking-opportunities-on-twitter

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Finding Speaking Opportunities On Twitter

Executes finding speaking opportunities on twitter 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
tweetLanguagestringOptional—ISO 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
authorstringOptional—Filter to a specific author handle
inReplyTostringOptional—Tweets replying to a specific handle
mentioningstringOptional—Tweets mentioning a specific handle
geotaggedNearstringOptional—Tweets near a location
withinRadiusstringOptional—Radius around geotaggedNear
geocodestringOptional—Lat/lng + radius string
placeObjectIdstringOptional—Tweets tagged with a place
minimumRetweetsnumberOptional—Minimum retweet count
minimumFavoritesnumberOptional—Minimum like count
minimumRepliesnumberOptional—Minimum reply count
startstringOptional—Tweets after this date (YYYY-MM-DD)
endstringOptional—Tweets before this date (YYYY-MM-DD)
includeSearchTermsbooleanOptionalfalseAdd the matched search term to each tweet
customMapFunctionstringOptional—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": ["call for speakers [TOPIC]", "looking for speakers [TOPIC]", "CFP [TOPIC]", "speaker applications [TOPIC]"],
  "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": ["call for speakers [TOPIC]", "looking for speakers [TOPIC]", "CFP [TOPIC]", "speaker applications [TOPIC]"], "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: KEYNOTE (large event, competitive) | PANEL (group format) | PODCAST_GUEST (audio interview) | VIRTUAL_SUMMIT (online event) | WORKSHOP (hands-on, smaller)

Step 4: Score Each Result

score = opportunity_score = (event_audience_size_signal: large=1, mid=0.6, small=0.3) * 0.40 + (deadline_is_future ? 1 : 0) * 0.35 + (topic_alignment ? 1 : 0.5) * 0.25

Step 5: Edge Cases

  • CFP deadlines are time-sensitive; flag any opportunity with a deadline in the past as EXPIRED; prioritize by days until deadline

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

# Finding Speaking Opportunities On Twitter
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.

관련 작업

같은 저장소의 Skills

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