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 skills add unifapi-agent/agents --skill linkedin-account-researchgit clone --depth 1 https://github.com/unifapi-agent/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/unifapi-agent/agents/linkedin-account-research)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00119 | $0.02083 |
| Opus 5 | $0.00060 | $0.01042 |
| Sonnet 5 | $0.00024 | $0.00417 |
| Haiku 4.5 | $0.00012 | $0.00208 |
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.
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 / firmographics —
linkedin/companies/{slug}— description, industry, headcount band, HQ, specialties; the spine of the snapshot. - Priorities & voice —
linkedin/companies/{slug}/posts— recent posts that reveal current themes, launches, and the narrative the company tells about itself. - Where they're investing —
linkedin/companies/{slug}/jobsandlinkedin/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 names —
linkedin/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 structure —
linkedin/companies/{slug}/affiliated— parent/subsidiary/affiliated entities, so a multi-entity account isn't read as one company. - Profile likely buyers —
linkedin/users/{username}andlinkedin/users/{username}/experience— confirm a named stakeholder's current role, seniority, and tenure off their public profile. - Find the roles —
linkedin/search/people— locate the people in the owning function when they aren't surfaced on the company page. - Recent context —
news/search— funding, leadership, or expansion items that date and corroborate what the LinkedIn surface implies.
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.
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.
- 9d ago First seen · 117 lines · 119 tokens per session scan A 5d294e7c357a
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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