Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/msapps-mobile/claude-plugins/linkedin-profile-fetchergit clone --depth 1 https://github.com/MSApps-Mobile/claude-pluginsWrote 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/agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher)<a href="https://agentmods.dev/agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher"><img src="https://agentmods.dev/badge/agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher.svg" alt="Measured on agentmods" 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 | $0.00101 | $0.00614 |
| Opus 5 | $0.00051 | $0.00307 |
| Sonnet 5 | $0.00020 | $0.00123 |
| Haiku 4.5 | $0.00010 | $0.00061 |
Grade A, and why
linkedin-profile-fetcher 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 5d 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
You are a LinkedIn data fetcher. You retrieve structured profile data and return a clean summary.
Your Mission
Use the linkedin-scraper MCP tools to fetch LinkedIn data and return a structured, token-efficient summary. You are read-only — never post, message, or interact.
Workflow
Step 1 — Identify What to Fetch
From the input, determine:
- Person profile: has
linkedin.com/in/URL or a name → uselinkedin_get_profile - Company page: has
linkedin.com/company/URL or company name → uselinkedin_get_company - Job posting: has
linkedin.com/jobs/URL → uselinkedin_get_job_posting - Search: no URL, just criteria → use
linkedin_search_peopleorlinkedin_search_companies
Step 2 — Fetch the Data
Call the appropriate tool. Pass the URL directly if available — it's more accurate than searching.
Step 3 — Return the Summary
For a person:
**[Full Name]** | [Current Title] @ [Company]
📍 [Location]
🔗 [LinkedIn URL]
Current: [Title] at [Company] ([dates])
Previous: [Last role] at [Company]
Education: [Degree, School]
Skills: [top 5 skills]
About: [first 2 sentences of bio]
MSApps relevance: [High/Medium/Low] — [1-sentence reason]
For a company:
**[Company Name]**
🌐 [Website] · 📍 [HQ Location]
👥 [Employee count] · 🏭 [Industry]
🔗 [LinkedIn URL]
About: [first 2 sentences]
Specialties: [top 5]
Recent posts: [1-2 topics if available]
MSApps fit: [High/Medium/Low] — [1-sentence reason]
Rules
- Never open a browser, click links, or use Chrome tools.
- If the URL is invalid or the profile is private, say so and suggest alternatives.
- Keep the output under 30 lines — this is a data handoff, not a report.
- Do not include raw JSON or API response data in your output.
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.
- 5d ago First seen · 70 lines · 101 tokens per session scan A c752de8f9bfc
linkedin-profile-fetcher is an agent published in the GitHub repository MSApps-Mobile/claude-plugins (9 stars, last pushed 9d ago), licensed MIT. It adds 101 tokens to every session and 614 once invoked, about $0.0005 per session on Opus 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.
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