Agent skill

Linkedin Account Research

by unifapi-agent in unifapi-agent/agents

When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles.

MITAuto-check passedSales & Support

Install Linkedin Account Research

skills CLI
$ npx skills add unifapi-agent/agents --skill linkedin-account-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install unifapi-agent/agents linkedin-account-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/lead-company-research-agent/linkedin-account-research .claude/skills/linkedin-account-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
linkedin-account-research
GitHub stars
589
Token cost
~2.5k tokens
SKILL.md length
1,039 words
Files
2
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles.

  • Works in 7 steps: Set the target. Take the company name… → Read the company surface. Pull… → Infer hiring signals. From… → …
  • Tasks that involve Sales call preparation
  • SKILL.md covers Use UnifAPI for live evidence, Workflow, Inferring the buying committee… and Output: account brief, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Linkedin Account Research is an agent skill from unifapi-agent/agents. When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles. Also use on "LinkedIn account research," "account brief," "research this company before a call," "who's the buying committee," "company priorities," "hiring signals," "discovery questions," "pre-call research," or "build a prospect dossier." Reads LinkedIn's public surface via URL slug only — read-only research, no private/logged-in data.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `README.md`).

It sits in Sales & Support, covering Sales call preparation. It works with LinkedIn. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.

When your agent uses it

  • Tasks that involve Sales call preparation

Example prompts

  • “LinkedIn account research,”
  • “account brief,”
  • “research this company before a call,”
  • “/linkedin-account-research”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Set the target. Take the company name and LinkedIn slug (and known stakeholder profile slugs if available). Note what the operator sells…
  2. Read the company surface. Pull linkedin/companies/{slug} (facts), linkedin/companies/{slug}/posts (priorities/voice), and…
  3. Infer hiring signals. From linkedin/companies/{slug}/jobs + linkedin/companies/{slug}/job-count, identify which functions are growing and…
  4. Map the likely buying committee. Use linkedin/companies/{slug}/people, linkedin/companies/{slug}/member-insights, linkedin/search/people…
  5. Layer recent context. Run news/search to date and corroborate priorities and to flag events worth a hook (hand off to account-news-signals…
  6. Write discovery questions grounded in the evidence, so the first call probes the real priorities rather than generic pain.
  7. Note source gaps. Call out where the public record is silent (private committee members, unstated budget) so the operator knows what still…

What it can do on your machine

Read from SKILL.md and the folder at commit fb53247. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Linkedin Account Research loads about 2.5k tokens when it runs. Until then it costs about 134 tokens; SKILL.md has 1,039 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~134
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 1,039 words, ~2,453 tokens.

Download SKILL.mdSave it as .claude/skills/linkedin-account-research/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
linkedin-account-research
description
When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles. Also use on "LinkedIn account research," "account brief," "research this company before a call," "who's the buying committee," "company priorities," "hiring signals," "discovery questions," "pre-call research," or "build a prospect dossier." Reads LinkedIn's public surface via URL slug only — read-only research, no private/logged-in data.
license
MIT
metadata.author
UnifAPI
metadata.version
1.0.0

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}/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 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} and linkedin/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.

UnifAPI reads public data only — it reads LinkedIn's public surface via URL slug, never private, logged-in, or connection-gated data, and never the operator's own LinkedIn account. Keep any billing metadata so the brief can state record cost.

Workflow

  1. Set the target. Take the company name and LinkedIn slug (and known stakeholder profile slugs if available). Note what the operator sells so the brief stays relevant. (Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists.)
  2. Read the company surface. Pull linkedin/companies/{slug} (facts), linkedin/companies/{slug}/posts (priorities/voice), and linkedin/companies/{slug}/affiliated (org shape) to extract stated priorities and the company's self-narrative.
  3. Infer hiring signals. From linkedin/companies/{slug}/jobs + linkedin/companies/{slug}/job-count, identify which functions are growing and what gaps the headcount implies — a cluster in one function names where the budget is going.
  4. Map the likely buying committee. Use linkedin/companies/{slug}/people, linkedin/companies/{slug}/member-insights, linkedin/search/people, and linkedin/users/{username} + linkedin/users/{username}/experience against the role-inference rules below — label each name confirmed (read off a public profile) or inferred (deduced from the org pattern, no profile seen).
  5. Layer recent context. Run news/search to date and corroborate priorities and to flag events worth a hook (hand off to account-news-signals for the full hook list).
  6. Write discovery questions grounded in the evidence, so the first call probes the real priorities rather than generic pain.
  7. Note source gaps. Call out where the public record is silent (private committee members, unstated budget) so the operator knows what still needs discovery.
Show full SKILL.md (452 more words)Show less

Inferring the buying committee from public roles

You will rarely see the whole committee on LinkedIn; infer it from the roles you can see (via linkedin/companies/{slug}/people / linkedin/search/people) plus the standard B2B buying pattern. Map each public title to a committee function:

Committee roleRead it fromTypical public titles
Economic buyerWho owns the budget line your product hitsVP / Director / Head of the owning function; C-level at smaller co's
ChampionWho feels the pain daily and would push internallyManager / Senior IC in the function; the person the new role reports to
Technical evaluatorWho must approve fit/security/integrationEng lead, IT, Security, RevOps, Data
End userWho uses it after purchaseICs in the function; the open-role title itself
BlockerWhose status quo or budget it threatensOwner of the incumbent tool/process; Procurement, Finance

Inference rules:

  • Company size sets committee size — under ~50 employees, one person often holds buyer + champion; at enterprise scale, expect a named procurement and security gate.
  • An open req is a committee clue: the role's reporting line names the likely champion, and its mandate names the economic buyer's priority.
  • Tenure matters — a new exec (under ~6 months, read from linkedin/users/{username}/experience) is more likely to fund change; a long-tenured incumbent may defend the status quo (potential blocker).
  • Never assert a committee role from a title alone as fact. Confirmed = a public profile actually shows the person and role; everything reasoned from org patterns is inferred.

Output: account brief

A one-page account brief, with the date generated:

markdown
# Account Brief — [Company] (generated YYYY-MM-DD)

## Snapshot

Industry · Headcount band · HQ · Specialties · Affiliated entities — each sourced to the public page.

## Current priorities & narrative

3–5 priorities, each tied to a recent post or page claim (link + date).

## Hiring signals

| Function | Open roles | Count trend | Gap implied | Source (job post) |
| -------- | ---------- | ----------- | ----------- | ----------------- |

## Likely buying committee

| Name / title                                              | Committee role | Confidence | Source |
| --------------------------------------------------------- | -------------- | ---------- | ------ |
| (or "unfilled — likely held by [function]" when inferred) |

## Discovery questions

5–8 questions, each tied to a specific piece of evidence above.

## Source gaps

What the public record doesn't show and the operator must confirm in discovery.

## Sources & record cost

Every URL + date pulled; UnifAPI billing metadata or estimate.
  • Every claim is cited to the public record it came from; inferred items are clearly flagged.
  • Discovery questions probe the evidenced priority, e.g. "You posted about consolidating tooling this quarter — where does [category] sit in that?" not generic pain-finding.

Guardrails

  • Read-only research. It builds the brief; it never sends connection requests, InMail, DMs, or any message — the operator owns all outreach from their own account.
  • Public data only. Reads LinkedIn's public surface via URL slug, never private, logged-in, or connection-gated data; never scrapes behind auth.
  • Confirmed vs inferred: headcount bands, committee roles, and priorities inferred from public signals are hypotheses — label inferred items so they are verified before they drive a pitch.
  • Profile data is for legitimate B2B research; respect each platform's rules and applicable privacy law.
  • Dated snapshots: profiles and pages age — note when a key signal (a role, a post) is more than a few months old, and re-pull to refresh.
  • account-news-signals (Lead Company Research Agent): layer recent news/funding/leadership events onto the brief for timing and hooks.
  • buying-signal-monitor (Social Selling Agent): catch real-time public intent that makes an account worth briefing in the first place.
  • unifapi: the shared data skill — connect MCP and discover the LinkedIn/news operations above.

© unifapi-agent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/lead-company-research-agent/linkedin-account-research of unifapi-agent/agents.

  • SKILL.md
  • README.md

Open the folder on GitHubat commit fb53247

Compare with similar skills

Linkedin Account Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Linkedin Account Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Linkedin Account Research this skillunifapi-agent/agents589—~2.5kAutomated safety check: PassMIT
Account Researchexplorium-ai/gtm-skills185—~2.8kAutomated safety check: PassMIT
Referral IntroTheCraigHewitt/skills159—~6.7kAutomated safety check: PassMIT
Company Researchliangdabiao/exa-research-mcp-skill110—~1.1kAutomated safety check: PassNone
Meeting Prepmgonto/executive-assistant-skills118—~4.5kAutomated safety check: PassNone
Company Researchliangdabiao/exa-research-mcp-skill110—~452Automated safety check: PassNone

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Works with

Categories

Questions about Linkedin Account Research

What does Linkedin Account Research do?

When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles. Linkedin Account Research is an agent skill from unifapi-agent/agents. When a seller, SDR, or founder wants a B2B account brief built from a company's public LinkedIn presence — its page, posts, open jobs, employees, and visible profiles.

When should I use Linkedin Account Research?

Linkedin Account Research fits situations like: tasks that involve Sales call preparation.

How do I install Linkedin Account Research in Claude Code?

Run `npx skills add unifapi-agent/agents --skill linkedin-account-research -a claude-code`. Or copy the skill folder (skills/lead-company-research-agent/linkedin-account-research in unifapi-agent/agents) into .claude/skills/linkedin-account-research in your project. Claude Code loads it when a task matches its description.

How do I install Linkedin Account Research in Codex?

Run `npx skills add unifapi-agent/agents --skill linkedin-account-research -a codex`. Or copy the skill folder (skills/lead-company-research-agent/linkedin-account-research in unifapi-agent/agents) into .agents/skills/linkedin-account-research in your project. Codex loads it when a task matches its description.

Can I use Linkedin Account Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add unifapi-agent/agents --skill linkedin-account-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkedin-account-research, .gemini/skills/linkedin-account-research, .github/skills/linkedin-account-research and .opencode/skills/linkedin-account-research in your project.

What does Linkedin Account Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Linkedin Account Research is instructions for the agent only.

Does Linkedin Account Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Linkedin Account Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Linkedin Account Research use?

Linkedin Account Research is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Linkedin Account Research use?

About 2.5k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Linkedin Account Research?

Skills that share tags, products or a category with Linkedin Account Research: Account Research (explorium-ai/gtm-skills, 185 stars), Referral Intro (TheCraigHewitt/skills, 159 stars), Company Research (liangdabiao/exa-research-mcp-skill, 110 stars) and Meeting Prep (mgonto/executive-assistant-skills, 118 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Linkedin Account Research?

unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 589 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.

Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.