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tracking-twitter-thought-leaders — Claude Code skill for X outreach

Finds Twitter/X accounts whose posts on a topic actually get shared, then hands back a ranked list for PR and partnerships.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add apidojo-io/apidojo-skills --skill tracking-twitter-thought-leaders -a claude-code
Open on GitHub

What tracking-twitter-thought-leaders does

The skill runs two apidojo scrapers in sequence: one searches tweets by topic keywords, the other pulls full profiles for the candidates it finds. You get name, handle, follower count, engagement rate, bio keywords and recent top tweets, sorted into three tiers from power voices down to rising accounts.

Follower count is not the ranking signal. The composite score multiplies average likes plus retweets on topic tweets by the follower-to-following ratio and the log of audience size, so an account with real discussion beats a silent million-follower profile. The skill suggests starting with a 5,000–2,000,000 follower range and a list of 25 to cut unknown accounts and mega-celebrities.

An APIFY_TOKEN environment variable has to be set before anything runs — the scrapers fail without it. You can drive the work through scripts/run_actor.js, an Apify MCP server, or the REST API, and save results as CSV or JSON. One loud mega-voice can swallow a whole topic, so the skill suggests excluding it and surfacing the next tier.

What it helps with

  • find active posters in a narrow niche on X
  • separate real engagement from inflated follower counts
  • build a 25-account outreach list
  • export candidate profiles to CSV
  • spot rising accounts worth an early partnership

Example prompts for the agent

  • find the top AI safety voices on Twitter, 25 accounts
  • build a list of B2B SaaS growth experts with high engagement
  • who is gaining followers fastest in climate tech

How to install tracking-twitter-thought-leaders

1Skills CLI (Vercel)

npx skills add apidojo-io/apidojo-skills --skill tracking-twitter-thought-leaders -a claude-code

2Manually: put the files in place

~/.claude/skills/tracking-twitter-thought-leaders/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/tracking-twitter-thought-leaders/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about tracking-twitter-thought-leaders

How do I install tracking-twitter-thought-leaders in Claude?

Run npx skills add apidojo-io/apidojo-skills --skill tracking-twitter-thought-leaders -a claude-code. The skill lands at ~/.claude/skills/tracking-twitter-thought-leaders/SKILL.md.

What does tracking-twitter-thought-leaders do?

It searches tweets by topic keywords, enriches the authors with profile data, and ranks them by topic engagement, follower-to-following ratio and audience size. The result is a table with handles, follower counts and recent tweets.

What do I need before running it?

An APIFY_TOKEN environment variable, which the apidojo scrapers check. The Apify MCP server is optional — you can also run it through scripts/run_actor.js or the REST API.

Source text

SKILL.md · Apache-2.0
---
name: tracking-twitter-thought-leaders
description: >
  Identifies and tracks thought leaders and key voices in any industry on Twitter/X using apidojo's scrapers.
  Triggers when the user asks to: find the top voices in an industry on Twitter, identify thought leaders
  in a niche, discover who has the most influence in a topic area on X, find experts tweeting about
  a subject, build a list of influencers to engage with on Twitter, track who is gaining followers
  fastest in a category, or identify key opinion leaders in a field for PR or partnership outreach.
  Returns name, handle, follower count, engagement rate, bio keywords, and recent top tweets.
  Ideal for PR teams, community managers, and B2B content marketers.
license: Apache-2.0
metadata:
  author: apidojo
  version: "1.0"
  apify-actors: apidojo/twitter-user-scraper, apidojo/tweet-scraper
---

# Tracking Twitter Thought Leaders

Finds Twitter/X accounts with genuine influence in a topic area — not just high follower counts, but accounts whose tweets get shared and discussed. Delivers a ranked list for PR outreach, community engagement, or partnership targeting.

## 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: Define topic, industry, and influence criteria
- [ ] Step 2: Search for topic-relevant tweets to find active voices
- [ ] Step 3: Enrich top accounts with profile data
- [ ] Step 4: Score by influence signals
- [ ] Step 5: Deliver ranked thought leader list
```

### Step 1: Clarify Parameters

Ask the user for:
- **Topic or industry** (e.g., "AI safety", "B2B SaaS growth", "climate tech")
- **Influence type** — broad reach (high followers), community depth (high engagement), or rising voices (growing fast)
- **Follower range** (default: 5,000–2,000,000 — excludes unknown accounts and mega-celebrities)
- **Geography/language** (optional)
- **List size** (default: 25)

### Step 2: Search for Topic Tweets

Find who's actively tweeting about the topic — recent activity matters more than old follower counts.


**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~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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]", "[TOPIC_KEYWORD_3]"],
  "maxItems": 300,
  "tweetLanguage": "en"
}
```

**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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"],
    "maxItems": 300
  }'
```

Extract unique `author.username` values from all results. Sort by their tweet's retweet+like count — accounts whose topic tweets get the most engagement are the most influential voices.

### Step 3: Enrich with Profile Data

Take top 100 candidate usernames. Fetch full profiles.

**If Apify MCP is available:**
```
Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "...up to 100"]
}
```

**REST API fallback:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["[username1]", "[username2]"]}'
```

### Step 4: Score by Influence

Calculate composite influence score for each account:

```
topic_engagement = avg(likes + retweets) on topic-related tweets
audience_quality = followers / following ratio (>1 is healthy)
influence_score = topic_engagement * log(followers) * audience_quality
```

Filter: keep only accounts within follower range AND whose bio suggests topical relevance.

### Step 5: Format Output

## Output Format

```
# Twitter Thought Leaders: [TOPIC/INDUSTRY]
Accounts analyzed: [N] | Final list: [N] | Date: [DATE]

## Top Thought Leaders

| # | Name | @Handle | Followers | Influence Score | Bio Excerpt | Recent Top Tweet |
|---|------|---------|-----------|-----------------|-------------|------------------|
| 1 | [name] | @[handle] | [N] | [score] | [bio] | "[tweet excerpt]" |

## Tier Breakdown

### 🏆 Power Voices (500K+ followers)
[list with brief bio and latest relevant tweet]

### 🎯 Core Influencers (50K–500K followers)
[list — best for outreach: big enough to matter, accessible enough to respond]

### 🌱 Rising Voices (5K–50K followers)
[list — early partnership opportunity, lower cost, high engagement]

## Best Accounts for Direct Outreach
[Top 5 picks with rationale — why they're ideal for PR, partnership, or co-content]

## Content Themes These Voices Tweet About
- [Theme 1]: [N] of the accounts tweet regularly about this
- [Theme 2]: [N] accounts
```

## Troubleshooting

**Results dominated by one person:** Some topics have one mega-voice. Exclude them and surface the next tier.
**Not enough topically relevant accounts:** Expand keyword list with synonyms, adjacent topic terms, and industry jargon.
**Follower counts seem off:** Cached data — for final list, spot-check top 5 accounts directly on Twitter.

© apidojo · Apache-2.0 · apidojo-io/apidojo-skills

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