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unifapi-agent
linkedin

When a workflow needs public LinkedIn data through UnifAPI — company pages, follower/employee counts, open jobs and job-count, member insights, people, posts, or engagement. Also use on "research this company on LinkedIn," "who works at," "open roles at," "LinkedIn profile for," "company posts," or when another skill (account research, news signal, buying signal, competitor profiling) needs the deterministic LinkedIn read path. Connect via the `unifapi` skill first. Read-only research, never connects or messages.

Overview

Publisherunifapi-agent
Repositoryagents
Skill namelinkedin
Stars
590
Forks
115
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Linkedin AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/unifapi-agent/agents/tree/main/skills/linkedin-agent/linkedin
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/linkedin-agent/linkedin .claude/skills/linkedin
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkedin on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkedin is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

linkedin

The deterministic read path for public LinkedIn data through UnifAPI. This is a Data Skill: it does not run a marketing job on its own — it names the concrete linkedin/... operations, response shapes, and gotchas so any B2B-first workflow (account research, news signals, buying signals, competitor profiling) reads from one known recipe instead of rediscovering the surface each time.

Read-only — eyes, not hands. It researches public LinkedIn data and returns cited records; it never connects, messages, or applies, and UnifAPI never holds LinkedIn credentials.

Use the unifapi skill for live evidence

Connect once through the shared unifapi skill (OAuth MCP), then call the operations below. Companies are keyed by their public {slug} (the vanity segment of the company URL) and people by their public {username} — read both from the LinkedIn URL, not a numeric id. Keep any billing metadata so the output can state record cost.

Response contract

Single-entity endpoints return the object in data:

json
{
  "request_id": "unif_...",
  "data": {},
  "billing": { "records_charged": 1, "balance_remaining": 99 }
}

List endpoints return an array in data plus pagination:

json
{
  "request_id": "unif_...",
  "data": [],
  "pagination": { "has_more": false, "next_cursor": null },
  "billing": { "records_charged": 1 }
}

When pagination.has_more is true, pass pagination.next_cursor as the next request's cursor. Always preserve billing when reporting cost.

Core operations

NeedOperation
Company pagelinkedin/companies/{slug}
Company headcount signallinkedin/companies/{slug}/job-count · .../jobs
Company peoplelinkedin/companies/{slug}/people
Member insightslinkedin/companies/{slug}/member-insights
Company postslinkedin/companies/{slug}/posts
Person profilelinkedin/users/{username} · .../about · .../experience
Person reachlinkedin/users/{username}/follower-count
Person posts / reactionslinkedin/users/{username}/posts · .../reactions
Search peoplelinkedin/search/people (?title=&current_company=&industry=)
Search jobs / postslinkedin/search/jobs · linkedin/search/posts
Job / post by idlinkedin/jobs/{id} · linkedin/posts/{id} (.../comments)
Resolve a geocode / industrylinkedin/search/locations · linkedin/search/industries

Need a field not listed here? Use the unifapi skill's get_operation to read the exact schema before calling — but pick the operation from this table, don't discover blind.

Workflow

The deterministic recipes. Pick the one that matches the job; each names exactly what to call.

  1. Resolve an account. Take the company {slug} from its LinkedIn URL, then call linkedin/companies/{slug} for follower_count, employee_count, industries, and headquarters.
  2. Size hiring as an investment signal. Call linkedin/companies/{slug}/job-count (returns a single total) and linkedin/companies/{slug}/jobs for the open roles — a rising count or a cluster of senior roles is a growth/priority signal.
  3. Map the buying committee. Call linkedin/companies/{slug}/people and linkedin/companies/{slug}/member-insights, or narrow with linkedin/search/people?current_company=...&title=... for specific roles.
  4. Read a person. Call linkedin/users/{username} plus .../about and .../experience; .../follower-count for reach (a separate LinkedinFollowerStats object); .../posts for what they publish.
  5. Read posts and engagement. Call linkedin/companies/{slug}/posts or linkedin/users/{username}/posts; each LinkedinPost carries like_count, comment_count, and share_count. Page via next_cursor.
  6. Search the surface. Use linkedin/search/people|jobs|posts with filters; resolve a geocode_location via linkedin/search/locations and an industry id via linkedin/search/industries first.
  7. Cite everything. Every claim ties back to the company, person, or post it came from; report billing.records_charged (or estimate when billing metadata is absent).

Shape notes

  • LinkedinCompany — keyed by {slug}. follower_count, employee_count, employee_count_range, industries, headquarters, is_verified.
  • LinkedinUser — keyed by {username}. Profile flags at top level: is_open_to_work, is_hiring, is_top_voice, is_creator, is_premium. Follower/connection counts are not here — read them from .../follower-count (LinkedinFollowerStats: follower_count, connection_count).
  • LinkedinPost — like_count, comment_count, share_count, reactions, author, post_type.
  • LinkedinJob — title, location, salary, level, employment_type, listed_at, company. LinkedinJobCount is just { total }.

Gotchas

  • Companies are keyed by {slug}, people by {username} — both read from the public LinkedIn URL, never a numeric id.
  • Follower and connection counts come from linkedin/users/{username}/follower-count, not the base profile object.
  • linkedin/search/people needs at least one filter — there is no all-of-LinkedIn dump.
  • A low balance can silently truncate list pages: check billing.truncated_due_to_balance — when true the page is partial, so top up before trusting any count computed from it.

Output

Return the records the calling workflow needs, each cited to its company, person, or post, plus a one-line cost note (records_charged). When this skill is used directly, a compact account brief is the default:

markdown
**{Company}** — {followers} followers, {employees} employees, {industry}. Open roles: {N} ({trend}). Recent posts: {engagement}. Likely buyers: {names/titles}. Evidence: {URLs}. Records: ~{N}.

Related skills

  • linkedin-account-research, account-news-signals (Lead & Company Research) — turn this read path into account briefs and news-tied signals.
  • buying-signal-monitor (Social Selling), competitor-profiling (Competitive Intelligence) — B2B intent and competitor work on top of LinkedIn reads.
  • unifapi — the shared data skill: connect MCP and look up exact schemas with get_operation.

Frequently asked questions

What does the Linkedin AI skill do?

When a workflow needs public LinkedIn data through UnifAPI — company pages, follower/employee counts, open jobs and job-count, member insights, people, posts, or engagement. Also use on "research this company on LinkedIn," "who works at," "open roles at," "LinkedIn profile for," "company posts," or when another skill (account research, news signal, buying signal, competitor profiling) needs the deterministic LinkedIn read path. Connect via the `unifapi` skill first. Read-only research, never connects or messages.

Why use Linkedin on TypingMind?

Because you install it once and use it with any model. Linkedin is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkedin in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/linkedin-agent/linkedin. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Linkedin?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkedin?

As many as you like. As long as a model supports skills, you can use Linkedin with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkedin AI skill free?

Yes. It is published on GitHub by unifapi-agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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