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-login/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-login)<a href="https://agentmods.dev/skills/maccman/growth-agents/linkedin-login"><img src="https://agentmods.dev/badge/skills/maccman/growth-agents/linkedin-login/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-login"><img src="https://agentmods.dev/badge/skills/maccman/growth-agents/linkedin-login.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 | $0.00072 | $0.00363 |
| Opus 5.5 | $0.00029 | $0.00145 |
| Sonnet 5.5 | $0.00014 | $0.00073 |
| Haiku 4.5 | $0.00007 | $0.00036 |
Grade A, and why
linkedin-login 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
LinkedIn Login (Browser Setup)
Opens LinkedIn's login page in the Cursor IDE browser. Once the user logs in, the session cookie persists automatically — no further setup needed for future LinkedIn scraping tasks.
Steps
- Open LinkedIn login in the browser:
browser_navigate → url: https://www.linkedin.com/login, take_screenshot_afterwards: true
- Tell the user:
"LinkedIn is now open in the browser. Please log in with your credentials. Once you're logged in and land on the LinkedIn feed, let me know and I'll confirm the session is active."
- Wait for the user to confirm they've logged in, then take a screenshot and check the URL:
browser_take_screenshot
If the URL is https://www.linkedin.com/feed/ or any non-login LinkedIn page, the login was successful.
- Confirm success:
"You're logged in! Your LinkedIn session is now saved in the Cursor browser. Future LinkedIn scraping tasks will work automatically."
Notes
- The Cursor browser persists cookies across sessions, so this is a one-time setup.
- If the user is redirected to a CAPTCHA or verification page, ask them to complete it and then confirm again.
- Do not attempt to fill in credentials automatically — let the user type them manually for security.
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 · 39 lines · 72 tokens per session scan A 5003d847eef3
linkedin-login is a skill published in the GitHub repository maccman/growth-agents (5 stars, last pushed 8mo ago), licensed MIT. It adds 72 tokens to every session and 363 once invoked, about $0.0003 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-login compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| linkedin-loginmaccman/growth-agents | Skill | 5 | 8mo ago | MIT | A | 72 | Cursor |
| interactive-loginyc-software/qm | Skill | 16k | yesterday | MIT | A | 46 | Claude Code, Codex |
| pinchtab-mcppinchtab/pinchtab | Skill | 11k | 10d ago | MIT | A | 52 | Claude Code, Codex |
| azure-messaging-webpubsub-javamicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 43 | Claude Code |
| cua-driverdavidondrej/skills | Skill | 4.3k | 3d ago | MIT | A | 68 | Claude Code, Codex |
| open-design-browsercompozy/compozy | Skill | 2.8k | yesterday | MIT | A | 56 | Claude Code, Codex |
| southwestborski/travel-hacking-toolkit | Skill | 895 | today | MIT | A | 40 | Claude Code |