Borrowing it
Nothing to install: this file belongs to maccman/growth-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/maccman/growth-agents/master/.cursor/skills/linkedin-scraping/SKILL.mdgit clone --depth 1 https://github.com/maccman/growth-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maccman/growth-agents/linkedin-scraping)<a href="https://agentmods.dev/skills/maccman/growth-agents/linkedin-scraping"><img src="https://agentmods.dev/badge/skills/maccman/growth-agents/linkedin-scraping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maccman/growth-agents/linkedin-scraping"><img src="https://agentmods.dev/badge/skills/maccman/growth-agents/linkedin-scraping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00408 |
| Opus 5.5 | $0.00019 | $0.00163 |
| Sonnet 5.5 | $0.00009 | $0.00082 |
| Haiku 4.5 | $0.00005 | $0.00041 |
Grade A, and why
linkedin-scraping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Scraping LinkedIn
LinkedIn blocks unauthenticated scrapers. Use the methods below in order of preference.
Method 1: WebFetch (fastest, free)
Try this first — no credits needed.
WebFetch: https://www.linkedin.com/in/<handle>/
Returns name, headline, about, full experience, education, skills, certifications, and recent activity.
Method 2: cursor-ide-browser MCP (visual/interactive)
Use when you need to scroll, screenshot, or interact with the page.
browser_navigate → url: https://www.linkedin.com/in/<handle>/, take_screenshot_afterwards: true
browser_lock
browser_scroll → ref: <element ref>, scrollIntoView: true # use browser_snapshot to find refs
browser_take_screenshot
browser_unlock
Always browser_lock before interacting, browser_unlock when done.
Method 3: Firecrawl browser (if credits available)
Check credits first with firecrawl --status. Skip if credits are negative.
firecrawl browser "open https://www.linkedin.com/in/<handle>/"
firecrawl browser "scrape" -o .firecrawl/linkedin-<handle>.md
firecrawl browser close
Use firecrawl browser (not firecrawl scrape) — LinkedIn requires browser automation.
First-Time Setup
If scraping returns a login wall, use the linkedin-login skill to authenticate the Cursor browser first. This is a one-time step.
Notes
- All methods rely on the user's existing LinkedIn session cookies — never handle credentials in code.
- Company pages (
/company/<slug>/) work the same way as profiles.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 54 lines · 47 tokens per session scan A f3a647435ffa
linkedin-scraping is a skill published in the GitHub repository maccman/growth-agents (5 stars, last pushed 8mo ago), licensed MIT. It adds 47 tokens to every session and 408 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
linkedin-scraping compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| linkedin-scrapingmaccman/growth-agents | Skill | 5 | 8mo ago | MIT | A | 47 | Cursor |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |