Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill discovering-pre-launch-startups-on-twitterPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
Discovering Pre Launch Startups On Twitter
Executes discovering pre launch startups on twitter using apidojo scrapers. Part of the apidojo intelligence skills library.
Prerequisites
APIFY_TOKENenvironment 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 outputStep 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 jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["launching soon [SECTOR]", "beta waitlist [SECTOR]", "building [SECTOR] product", "#buildinpublic [SECTOR]", "soft launch [SECTOR]"],
"maxItems": 100
}REST API fallback:
bash
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["launching soon [SECTOR]", "beta waitlist [SECTOR]", "building [SECTOR] product", "#buildinpublic [SECTOR]", "soft launch [SECTOR]"], "maxItems": 100}'Wait for SUCCEEDED. Fetch dataset:
bash
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"Step 3: Classify Results
classification: WAITLIST (accepting signups) | BETA (active testing) | STEALTH (building but not sharing product) | SOFT_LAUNCH (live but not announced widely)Step 4: Score Each Result
score = pre_launch_score = (waitlist_signal ? 1 : 0) * 0.40 + (build_in_public_signal ? 1 : 0) * 0.30 + (followerCount < 5000 ? 1 : 0.5) * 0.20 + (tweeted_in_last_14_days ? 1 : 0) * 0.10Step 5: Edge Cases
- Pre-launch startups may tweet inconsistently; check last 10 tweets for product updates rather than bio alone to confirm active development
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
# Discovering Pre Launch Startups 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.
