monitoring-vc-investor-activity-on-twitter
Monitors VC investor activity and deal signals on Twitter using apidojo's Tweet scraper. Triggers when the user asks to: monitor VC activity on Twitter, track what investors are tweeting about, find VCs who are actively investing in a sector on X, monitor deal flow signals from investor tweets, find investors who are looking for deals in a space, track funding announcements from VC firms on Twitter, or discover which investors are most active in a specific vertical. Returns investor handle, firm, investment thesis signals, recent activity, deal flow indicators. Ideal for startup founders fundraising, co-investors, and startup ecosystem analysts.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-vc-investor-activity-on-twitter- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads monitoring-vc-investor-activity-on-twitter automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Monitoring Vc Investor Activity On Twitter
Executes monitoring vc investor activity 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 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~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_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["investing in [SECTOR]", "excited about [SECTOR]", "looking for [SECTOR] startups", "portfolio company [SECTOR]"],
"maxItems": 100
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["investing in [SECTOR]", "excited about [SECTOR]", "looking for [SECTOR] startups", "portfolio company [SECTOR]"], "maxItems": 100}'
Wait for SUCCEEDED. Fetch dataset:
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"
Step 3: Classify Results
classification: ACTIVE_INVESTOR (recent deal activity, thesis tweets) | PASSIVE_FOLLOWER (investor title but no deal signals) | ANGEL (individual, smaller checks) | FIRM_PARTNER (major VC firm affiliation)
Step 4: Score Each Result
score = deal_signal_score = (tweeted_investment_thesis ? 1 : 0) * 0.35 + (tweeted_about_portfolio ? 1 : 0) * 0.25 + (bio_contains_VC_firm ? 1 : 0) * 0.25 + (followerCount > 5000 ? 1 : 0.5) * 0.15
Step 5: Edge Cases
- Distinguish actual investment partners from associates and VPs — partners make investment decisions; use title from bio for classification
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
# Monitoring Vc Investor Activity 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.
FAQ
What does the monitoring-vc-investor-activity-on-twitter skill do?
Monitors VC investor activity and deal signals on Twitter using apidojo's Tweet scraper. Triggers when the user asks to: monitor VC activity on Twitter, track what investors are tweeting about, find VCs who are actively investing in a sector on X, monitor deal flow signals from investor tweets, find investors who are looking for deals in a space, track funding announcements from VC firms on Twitter, or discover which investors are most active in a specific vertical. Returns investor handle, firm, investment thesis signals, recent activity, deal flow indicators. Ideal for startup founders fundraising, co-investors, and startup ecosystem analysts.
How do I install monitoring-vc-investor-activity-on-twitter?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-vc-investor-activity-on-twitter in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/monitoring-vc-investor-activity-on-twitter/ (personal) or .claude/skills/monitoring-vc-investor-activity-on-twitter/ (project).
Which AI agents can use monitoring-vc-investor-activity-on-twitter?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is monitoring-vc-investor-activity-on-twitter safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains monitoring-vc-investor-activity-on-twitter?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 797 recorded installs.
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