discovering-pre-launch-startups-on-twitter

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Discovers pre-launch startups and products on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: find pre-launch startups on Twitter, discover companies building in stealth mode on X, find products in beta or waitlist mode on Twitter, identify early-stage startups before they launch publicly, find founders building in public before launch, discover startup waitlists or beta invites on Twitter, or research what new companies are building in a space. Returns startup handle, product description, waitlist/launch signals, stage, and niche. Ideal for VCs scouting early deals, accelerator scouts, and competitive intelligence teams.

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Sourceapidojo-io/apidojo-skills

Added on2026-09-03

NPX Install

npx skill4agent add apidojo-io/apidojo-skills discovering-pre-launch-startups-on-twitter

SKILL.md Content

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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_TOKEN
```
environment 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 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": ["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) * 0.10

Step 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.

discovering-pre-launch-startups-on-twitter

NPX Install

Tags

SKILL.md Content

Discovering Pre Launch Startups On Twitter

Prerequisites

Inputs

Workflow

Step 2: Run the Actor

Step 3: Classify Results

Step 4: Score Each Result

Step 5: Edge Cases

Output Format

Troubleshooting