Imported from apidojo-io/apidojo-skills (skills/intent/building-twitter-industry-watchlist/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill building-twitter-industry-watchlist. Copyright stays with the author (Apache-2.0).

Building a Twitter Industry Watchlist

Name: building-twitter-industry-watchlist
Author: apidojo-io

Identifies highest-signal Twitter accounts in an industry — people whose tweets consistently generate discussion, surface new information, or shape thinking in the space.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile or tweet URLs
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`twitterUserIds`	array	Optional	`[]`	Twitter user IDs
`getFollowers`	boolean	Optional	`false`	Extract follower lists
`getFollowing`	boolean	Optional	`false`	Extract following lists
`getRetweeters`	boolean	Optional	`false`	Extract retweeters of a tweet URL
`includeUnavailableUsers`	boolean	Optional	`false`	Include unavailable/suspended users
`maxItems`	number	Optional	Unlimited	Maximum users to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search for high-engagement industry tweets
- [ ] Step 2: Collect influential account handles
- [ ] Step 3: Enrich and score
- [ ] Step 4: Classify by account type
- [ ] Step 5: Deliver curated watchlist

Step 1: Search Industry Conversations

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~tweet-scraper"
Input:
{
  "searchTerms": ["[INDUSTRY]", "#[industry]", "[INDUSTRY] trends", "[INDUSTRY] analysis"],
  "maxItems": 500
}

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": ["venture capital", "#vc", "VC trends 2026"], "maxItems": 500}'

Collect authors with likeCount + replyCount >= 10 on their industry tweets.

Step 2: Score Influence

signal_score = (retweets / followers * 1000) * 0.35
             + (replies / followers * 1000) * 0.30
             + min(followers / 100000, 1) * 0.20
             + (tweeted_industry_content >= 3 in 30 days ? 1 : 0) * 0.15

Account type from bio:

FOUNDER: "founder", "CEO", "built"
INVESTOR: "partner", "VC", "investor"
ANALYST: "analyst", "researcher", "writer"
JOURNALIST: known pub or "reporter", "journalist"
PRACTITIONER: role title at company

Step 3: Edge Cases

Bot accounts: retweetCount >> likeCount → flag if retweets > 5× likes
Ambiguous type: Use PRACTITIONER as default when unclear
Multiple accounts from same company: Keep the most influential one

Output Format

# [INDUSTRY] Twitter Watchlist
Accounts: [N] | Date: [DATE]

## Founders & Operators
| Name | @Handle | Role | Followers | Avg Likes | Signal Score |
|------|---------|------|-----------|----------|-------------|

## Investors & Analysts
| Name | @Handle | Role | Followers | Signal Score |
|------|---------|------|-----------|-------------|

## Press & Media
| Name | @Handle | Publication | Followers | Signal Score |
|------|---------|------------|-----------|-------------|

## How to Create Twitter List
Go to Twitter → Lists → Create List → Add members by username

Troubleshooting

Results are news not insiders: Use #[industry] hashtag to find community members vs. general readers. Too many promotional accounts: Filter accounts where > 50% of tweets include external links. Watchlist too large: Apply score cutoff ≥ 0.60; keep ≤ 40 accounts for daily readability.

{ "ocm": "1", "id": "apidojo-io-apidojo-skills-building-twitter-industry-watchlist", "kind": "skill", "name": "building-twitter-industry-watchlist", "description": "Builds a curated Twitter industry watchlist of key voices using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: build a Twitter watchlist for an industry, find key Twitter accounts to follow in a niche, create a curated list of thought leaders in a sector on X, identify the most influential Twitter accounts in a business category, build a Twitter list for industry monitoring, find the signal-to-noise accounts in a topic area, or compile the must-follow accounts for staying current in an industry. Returns account list with handle, follower count, engagement rate, topic focus, and influence score. Ideal for business analysts, investors, executives, and professionals doing industry intelligence.", "publisher": "apidojo-io", "version": "1.0.0", "capabilities": { "domains": [ "coding" ], "tags": [ "skill-md", "skills-sh" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Builds a curated Twitter industry watchlist of key voices using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: build a Twitter watchlist for an industry, find key Twitter accounts to follow in a niche, create a curated list of thought leaders in a sector on X, identify the most influential Twitter accounts in a business category, build a Twitter list for industry monitoring, find the signal-to-noise accounts in a topic area, or compile the must-follow accounts for staying current in an industry. Returns account list with handle, follower count, engagement rate, topic focus, and influence score. Ideal for business analysts, investors, executives, and professionals doing industry intelligence." ], "primary": false, "metadata": { "source": { "provider": "skills.sh", "repository": "https://github.com/apidojo-io/apidojo-skills", "path": "skills/intent/building-twitter-industry-watchlist/SKILL.md", "ref": "HEAD", "url": "https://github.com/apidojo-io/apidojo-skills/blob/HEAD/skills/intent/building-twitter-industry-watchlist/SKILL.md", "key": "apidojo-io/apidojo-skills/skills/intent/building-twitter-industry-watchlist/SKILL.md" }, "license": "Apache-2.0" }, "instructions": "# Building a Twitter Industry Watchlist\n\nIdentifies highest-signal Twitter accounts in an industry — people whose tweets consistently generate discussion, surface new information, or shape thinking in the space.\n\n## Prerequisites\n\n- `APIFY_TOKEN` environment variable set\n- Optional: Apify MCP server installed\n\n## Inputs\n\n| Parameter | Type | Required | Default | Notes |\n|-----------|------|----------|---------|-------|\n| `startUrls` | array | Optional | `[]` | Twitter profile or tweet URLs |\n| `twitterHandles` | array | Optional | `[]` | Twitter usernames (without @) |\n| `twitterUserIds` | arr", "cost": { "context_tokens": 1074 } }

building-twitter-industry-watchlist

About

Building a Twitter Industry Watchlist

Prerequisites

Inputs

Workflow

Step 1: Search Industry Conversations

Step 2: Score Influence

Step 3: Edge Cases

Output Format

Troubleshooting

Use it

Manifest

Reviews