Imported from apidojo-io/apidojo-skills (skills/intent/tracking-startup-founders-on-twitter/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill tracking-startup-founders-on-twitter. Copyright stays with the author (Apache-2.0).

Tracking Startup Founders On Twitter

Executes tracking startup founders on twitter using apidojo scrapers. Part of the apidojo intelligence skills library.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run twitter-user-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):

# 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 json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "searchTerms": ["founder [SECTOR]", "building [SECTOR] startup", "CEO [SECTOR]", "#buildinpublic [SECTOR]"],
  "maxItems": 100
}

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 '{"searchTerms": ["founder [SECTOR]", "building [SECTOR] startup", "CEO [SECTOR]", "#buildinpublic [SECTOR]"], "maxItems": 100}'

Wait for SUCCEEDED. Fetch dataset:

curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"

Step 3: Classify Results

classification: EARLY_STAGE (< 1K followers, building in public) | SEED_STAGE (1K-10K, active fundraising signals) | GROWTH_STAGE (> 10K, scaling)

Step 4: Score Each Result

score = founder_signal_score = (bio_contains_founder_title ? 1 : 0) * 0.35 + (recent_product_tweet ? 1 : 0) * 0.35 + min(followerCount/10000, 1) * 0.30

Step 5: Edge Cases

• Many 'founder' accounts are actually solopreneurs or side-project builders — not venture-backed; verify by looking for funding mentions or accelerator affiliations in bio

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

# Tracking Startup Founders 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.