Agent skill · Backend & API

social-media-finder-skill

This skill helps users automatically find social media profiles across platforms like Facebook, Twitter, Instagram, LinkedIn, etc. using the BrowserAct API. Agent should proactively apply this skill when users express needs like finding someone's social media accounts, discovering a brand's social media presence, tracking down social profiles of job candidates, finding contact info for sales prospects, researching a person's digital footprint, verifying the identity of someone met online, monitoring the social media reach of influencers, checking social accounts of business competitors, gather

browser-actgithub.com/browser-actGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add browser-act/skills --skill social-media-finder-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: yes
Path: solutions/lead-generation/social-media-finder-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 5,160
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Social Media Finder Skill ## 📖 Brief This skill automates the discovery of social media accounts associated with individuals, brands, or businesses across multiple platforms. By providing a name, it searches across Facebook, Twitter, Instagram, LinkedIn, TikTok, and more — extracting profile URLs, usernames, follower counts, and bio snippets in one go. The results are returned as a downloadable CSV file. ## ✨ Features 1. **No Hallucinations**: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction. 2. **No Captcha Issues**: No need to handle reCAPTCHA or other verification challenges. 3. **No IP Restrictions**: No need to handle regional IP restrictions or geofencing. 4. **Faster Execution**: Tasks execute faster compared to pure AI-driven browser automation solutions. 5. **Cost-Effective**: Significantly lowers data acquisition costs compared to high-token-consuming AI solutions. ## 🔑 API Key Setup Before running, check the `BROWSERACT_API_KEY` environment variable. If not set, do not take other measures; ask and wait for the user to provide it. **Agent must inform the user**: > "Since you haven't configured the BrowserAct API Key yet

What's inside
Steps it walks through
  1. 📖 Brief
  2. ✨ Features
  3. 🔑 API Key Setup
  4. 🛠️ Input Parameters
  5. 🚀 Invocation Method
  6. ⏳ Execution Monitoring
  7. 📊 Data Output
  8. ⚠️ Error Handling & Retry
  9. 🌟 Typical Use Cases
Ships with 1 file
  • scripts/social_media_finder.py
Commands it runs
Example invocation
python -u ./scripts/social_media_finder.py "John+Smith"
More from skills
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About this skill
What does the social-media-finder-skill skill do?

This skill helps users automatically find social media profiles across platforms like Facebook, Twitter, Instagram, LinkedIn, etc. using the BrowserAct API. Agent should proactively apply this skill when users express needs like finding someone's social media accounts, discovering a brand's social media presence, tracking down social profiles of job candidates, finding contact info for sales prospects, researching a person's digital footprint, verifying the identity of someone met online, monitoring the social media reach of influencers, checking social accounts of business competitors, gather

How do I install it?

Run `npx skills add browser-act/skills --skill social-media-finder-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From browser-act/skills, a repository with 5,160 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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