linkedin-profile-fetcher

linkedin-profile-fetcher is an agent for coding agents from MSApps-Mobile/claude-plugins. It costs 101 tokens per session (614 once invoked), scanned A, original, MIT.

A read-only LinkedIn data fetcher that returns structured information about a person, company, job posting, or search result. It uses LinkedIn scraper tools and does not post or send messages.

In plain words
What is it for?
For looking up LinkedIn profiles, company pages, job postings, or people and companies matching search criteria.
Why use it?
It gives other workflows consistent LinkedIn data before outreach, enrichment, or research.

Agent

Part of the linkedin-scraper plugin — 3 skills, 1 agent shipped together

Install

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.

agentmods
npx agentmods add agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher
Clone the repo
git clone --depth 1 https://github.com/MSApps-Mobile/claude-plugins

Or install linkedin-scraper, the plugin that ships this one along with the rest of its 3 skills, 1 agent.

Wrote 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.

agentmods badge for linkedin-profile-fetcher

README.md
[![agentmods](https://agentmods.dev/badge/agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher.svg)](https://agentmods.dev/agents/msapps-mobile/claude-plugins/linkedin-profile-fetcher)
Your own site
<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>
Per session 101 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 614 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash c752de8f9bfc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/linkedin-scraper/agents/linkedin-profile-fetcher.md · 70 lines

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 → use linkedin_get_profile
  • Company page: has linkedin.com/company/ URL or company name → use linkedin_get_company
  • Job posting: has linkedin.com/jobs/ URL → use linkedin_get_job_posting
  • Search: no URL, just criteria → use linkedin_search_people or linkedin_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.
Changes

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.

  1. 5d ago First seen · 70 lines · 101 tokens per session scan A c752de8f9bfc

Subscribe to this mod's changes

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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