Aprimorada · dados ao vivoLinkedIn Agent

LinkedIn Data Skill

Leia dados públicos do LinkedIn via UnifAPI MCP: as operações linkedin/... exatas e formatos de resposta para pesquisa B2B. Nunca conecta nem envia DMs.

Nativa para agents

Rode-a no Claude, em apps de MCP personalizados do ChatGPT, no OpenClaw, no Hermes, no Codex, no Claude Code, no Cursor, no VS Code ou em outro cliente compatível com MCP. Sem fluxo de GUI dedicado e sem chave de API de LLM separada.

Respaldada por dados públicos ao vivo

Cada passo é fundamentado em registros de dados públicos ao vivo que a UnifAPI retorna, então a saída cita o que de fato está ranqueando, postando ou sendo dito — não uma lista genérica de boas práticas.

Composável e open source

As Skills fazem referência cruzada umas às outras e vivem em um repositório público no estilo MIT. Leia o SKILL.md completo no GitHub, faça um fork ou rode-o como está dentro do seu agent.

Prompt de execução

Cole isto no Codex ou Claude Code

O prompt é intencionalmente editável. Substitua os handles, o mercado, o orçamento e o objetivo da campanha, e então deixe o agent chamar o MCP da UnifAPI quando precisar de dados públicos ao vivo.

Using the LinkedIn Data Skill, research stripe on public LinkedIn: pull the company profile and recent posts, run a company-filtered job search for one relevant keyword, and return a sourced brief with unsupported capabilities called out. Read-only.
Como a skill funciona

A skill completa, renderizada a partir do seu SKILL.md

Use this skill to read the eight LinkedIn operations currently published by UnifAPI. Treat this list as the capability boundary: do not invent legacy sub-resources or promise people search, keyword post search, company employee listings, reactions, follower counts, or job counts.

Use UnifAPI for live evidence

Connect with the unifapi skill, inspect the chosen operation with get_operation, then call it with call_api. Successful responses use UnifAPI's standard envelope:

{
  "data": {},
  "pagination": { "has_more": false, "next_cursor": null },
  "billing": { "records_charged": 1 }
}

Keep billing in cost reporting. For list operations, pass pagination.next_cursor as the next request's cursor only when pagination.has_more is true.

Core operations

NeedOperation
Company profilelinkedin/companies/{slug}
Company postslinkedin/companies/{slug}/posts
Member profilelinkedin/users/{username}
Member postslinkedin/users/{username}/posts
Search jobslinkedin/search/jobs
Job detaillinkedin/jobs/{id}
Post detaillinkedin/posts/{id}
Post commentslinkedin/posts/{id}/comments

Workflow

  1. Resolve the public key. Read a company {slug} from /company/<slug> or a member {username} from /in/<username>.
  2. Inspect the contract. Call get_operation before the first live call so query fields, pagination, response shape, and billing are current.
  3. Read the narrowest record. Start with the company or member profile; add its posts only when the task needs current themes or authored evidence.
  4. Research hiring carefully. Read company_id from the company profile, then call linkedin/search/jobs with a meaningful keyword and the numeric company filter. This is a filtered search, not an exhaustive company job list or a historical job-count series.
  5. Open evidence. Use a returned job id with linkedin/jobs/{id} or a post id with linkedin/posts/{id}; fetch comments only when reception matters.
  6. Report limits. Separate fields read directly from hypotheses inferred from posts, jobs, or company size. State when a requested capability is outside the current eight-operation surface.

Shape notes

  • Company profiles expose the URL slug plus company_id, description, industry, size/headcount fields, headquarters, specialties, and public counts when present.
  • Member profiles expose the URL username, headline, location, website, and public status flags. Follower and connection counts are not part of the current profile contract.
  • Job search is cursor-paginated and requires a free-text keyword. The company filter accepts the numeric company_id from a company profile.
  • Post lists and comments are cursor-paginated. Preserve returned ids and source URLs so every downstream claim is traceable.

Gotchas

  • Companies are keyed by URL slug; members are keyed by URL username. Do not substitute display names.
  • A company profile's headcount is a current public field, not proof of growth over time.
  • A filtered job result is evidence that a matching role was returned, not proof of the company's complete hiring plan.
  • Capacity-limited collection and job operations may retry inside the gateway. Do not add a second retry loop unless the returned error contract says it is safe.
  • If an operation is absent from list_operations, treat it as unavailable rather than guessing an older path.

Output

Return the records the caller needs with source URLs, dates where available, a clear distinction between observed facts and inference, and one cost line using billing.records_charged.

  • linkedin-account-research — turns company profiles, posts, filtered job results, named member profiles, and news into an account brief.
  • account-news-signals — corroborates dated company news against official company posts.
  • buying-signal-monitor — combines X intent search with named-account LinkedIn evidence.
  • unifapi — connects MCP and exposes the current operation catalog and schemas.

Fonte: linkedin/SKILL.md no GitHub — abra um PR lá para melhorá-la.

Ferramentas de dados públicos

As APIs ao vivo que esta skill chama

Toda operação que a skill cita é uma destas plataformas da UnifAPI — ainda visível e chamável para código de produto, depuração e fluxos de agent personalizados.

  • As operações `linkedin/...` concretas para cada tarefa comum de leitura B2B
  • Notas sobre o contrato de resposta e o formato para empresas, usuários, vagas e posts
  • Receitas determinísticas: pesquisa de contas, evidência direcionada de contratação, membros e posts
  • Armadilhas da plataforma (chaves de slug vs. username, paginação por cursor, cobrança)