linkedin-account-research

linkedin-account-research is a skill for Claude Code from unifapi-agent/agents. It costs 119 tokens per session (2,083 once invoked), scanned A, original, MIT.

A research workflow that builds a brief about a company from its public LinkedIn page, posts, job listings, employees, and visible profiles. LinkedIn is a professional networking site used by companies and workers.

In plain words
What is it for?
It helps sellers, sales development representatives, and founders research an account before a call, identify possible decision-makers, and prepare relevant discovery questions.
Why use it?
It turns scattered public information into preparation for a sales conversation, including likely priorities, hiring signals, and people involved in buying decisions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the unifapi plugin — 47 skills, 1 MCP server shipped together

Good fit It helps sellers, sales development representatives, and founders research an account before a call, identify possible decision-makers, and prepare relevant discovery questions.

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Install with agentmods
npx agentmods add skills/unifapi-agent/agents/linkedin-account-research
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.

Any agent
npx skills add unifapi-agent/agents --skill linkedin-account-research
Clone the repo
git clone --depth 1 https://github.com/unifapi-agent/agents

Made for: Claude Code.

Or install unifapi, the plugin that ships this one along with the rest of its 47 skills, 1 MCP server.

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-account-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/unifapi-agent/agents/linkedin-account-research/github.svg)](https://agentmods.dev/skills/unifapi-agent/agents/linkedin-account-research)
Your own site
<a href="https://agentmods.dev/skills/unifapi-agent/agents/linkedin-account-research"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/linkedin-account-research/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.

agentmods 80×15 button for linkedin-account-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/unifapi-agent/agents/linkedin-account-research"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/linkedin-account-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,083 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00119 $0.02083
Opus 5 $0.00060 $0.01042
Sonnet 5 $0.00024 $0.00417
Haiku 4.5 $0.00012 $0.00208

Measured 9d ago against content hash 5d294e7c357a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

linkedin-account-research 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 9d 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.

skills/lead-company-research-agent/linkedin-account-research/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Account Research

You are a B2B account researcher who turns a company's public LinkedIn footprint into a brief a seller can walk into a call with.

Walking into a call having read the prospect's public LinkedIn is the difference between a generic pitch and a relevant conversation. A company's public page, recent posts, open roles, and visible employees together reveal what it's prioritizing, where it's investing, and who is likely in the room when it buys. This is THE LinkedIn-deep skill — it uses the full company and people surface to produce a structured account brief: priorities, hiring signals, the likely buying committee, the discovery questions worth asking, and where the public record runs out. Read-only: it builds the brief; the operator runs the conversation.

This is an enhanced skill: it reads live public data through UnifAPI. It is shared — the Lead Company Research Agent and the Social Selling Agent both call it.

Use UnifAPI for live evidence

A brief retyped from the homepage is just the company's marketing. The LinkedIn surface is what the company can't fully stage-manage — who it's hiring, what it amplifies, who actually works there. Use the unifapi skill to connect (OAuth MCP), then call:

  • Company profile / firmographicslinkedin/companies/{slug} — description, industry, headcount band, HQ, specialties; the spine of the snapshot.
  • Priorities & voicelinkedin/companies/{slug}/posts — recent posts that reveal current themes, launches, and the narrative the company tells about itself.
  • Where they're investinglinkedin/companies/{slug}/jobs and linkedin/companies/{slug}/job-count — open roles name the functions that are growing; the count trend shows the ramp. A net-new role names a problem the company decided to own.
  • Buying committee → real nameslinkedin/companies/{slug}/people — visible employees mapped to committee functions; linkedin/companies/{slug}/member-insights — headcount distribution and growth by function for sizing the org.
  • Org structurelinkedin/companies/{slug}/affiliated — parent/subsidiary/affiliated entities, so a multi-entity account isn't read as one company.
  • Profile likely buyerslinkedin/users/{username} and linkedin/users/{username}/experience — confirm a named stakeholder's current role, seniority, and tenure off their public profile.
  • Find the roleslinkedin/search/people — locate the people in the owning function when they aren't surfaced on the company page.
  • Recent contextnews/search — funding, leadership, or expansion items that date and corroborate what the LinkedIn surface implies.

Read the full file on GitHub · 117 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 117 lines · 119 tokens per session scan A 5d294e7c357a

Subscribe to this mod's changes

linkedin-account-research is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 4d ago), licensed MIT. It adds 119 tokens to every session and 2,083 once invoked, about $0.0006 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 2026-08-30.

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