SocQ LinkedIn Research

SKILL.md

---
name: socq-linkedin-research
description: Research public LinkedIn content, accounts, keywords, and performance data with SocQ. Use when an agent needs LinkedIn-specific discovery, collection, endpoint selection, credit estimates, asynchronous task execution, pagination, or raw exports through the SocQ CLI, MCP, or REST API.
metadata:
  openclaw:
    homepage: https://github.com/SocQAPI/socq-devtools
    primaryEnv: SOCQ_API_KEY
    requires:
      env:
        - SOCQ_API_KEY
      anyBins:
        - socq
        - npx
    envVars:
      - name: SOCQ_API_KEY
        required: true
        description: SocQ API key used to authenticate CLI, MCP, and REST requests.
    install:
      - kind: node
        package: "@socq/cli"
        bins:
          - socq
---

# SocQ LinkedIn Research

## SocQ Links

- Website: <https://socq.ai/>
- Platform page: <https://socq.ai/apis/linkedin>
- API documentation: <https://docs.socq.ai/api-manual/linkedin>
- API key: <https://socq.ai/dashboard/api-key>
- MCP and CLI: <https://docs.socq.ai/integrations/overview>
- Agent Skill guide: <https://docs.socq.ai/integrations/skill>

Use this Skill to select and run SocQ endpoints for public LinkedIn research. SocQ requests are asynchronous and credit-metered.

## Use When

- The user asks to discover, collect, compare, or analyze public LinkedIn data.
- The workflow needs LinkedIn-specific endpoint selection, input validation, pagination, or raw exports.
- An agent must estimate credits, submit a task, poll it to completion, and report normalized results.

## Endpoint Selection

- Read [platform.md](references/platform.md) before selecting an endpoint or constructing input. It contains the current endpoint IDs, typed MCP tools, CLI mappings, costs, input choices, and validated examples generated from the Capability Registry.
- Choose from the shape of the requested resource; do not route every request through broad search.
- Prefer direct URLs, canonical usernames, or platform IDs when the user supplies them.
- Re-read the live schema after a validation error instead of inventing parameters.

## Key Inputs

- Preserve the user's entities, query terms, date range, locale, filters, ordering, and requested result limit.
- Ask only for missing input required by the selected endpoint.
- Use `view: "standard"` for MCP and Skill result reads. Use `_result_view: "standard"` for typed MCP tools or `--result-view standard` for CLI output.
- Add a reusable idempotency key when a submission might be retried.
- Treat `next_cursor` as opaque and continue only until the requested scope or user-approved cap is reached.

## Execution

1. Prefer an already configured SocQ MCP server at `https://api.socq.ai/mcp?platforms=linkedin`; use the typed tool listed in [platform.md](references/platform.md), or compact `socq_execute` when needed.
2. If MCP is unavailable, use `socq` or `npx @socq/cli`. Use REST only as the final fallback.
3. Read [authentication.md](references/authentication.md), keep `SOCQ_API_KEY` in the environment, and never put it in prompts, URLs, committed files, or retained commands.
4. Read [billing.md](references/billing.md), report the expected cost, and obtain confirmation before a paid large-volume or multi-endpoint run.
5. Submit with `_request_source: "skill"`, `--request-source skill`, or `X-Socq-Source: skill-rest` for MCP, CLI, or REST.
6. Save the returned task ID. Treat `queued` and `running` as incomplete and follow [async-tasks.md](references/async-tasks.md) until `succeeded` or `failed`.
7. Follow [pagination.md](references/pagination.md) for all requested pages. Retrieve task files when complete raw JSONL output is required.
8. Read [errors.md](references/errors.md) before retrying authentication, credit, rate-limit, validation, or provider failures.

## Output Expectations

Include:

- selected endpoint and execution path (MCP, CLI, or REST)
- concise input and filter summary
- expected and reported credit usage when available
- task ID and terminal status
- result count, pages read, and whether more data remains
- normalized findings or the raw export location
- collection time, failed requests, unsupported filters, and incomplete coverage

## Guardrails

- Collect only public data supported by the selected endpoint.
- Do not retry a failed paid request blindly; inspect the normalized error first.
- Do not start a paid large-volume or multi-endpoint run without user confirmation.
- Do not claim completeness when pagination stops early, a provider fails, or a requested filter is unsupported.
- Do not compare metrics collected with different date windows, filters, locales, or content types without labeling the difference.
- Keep task IDs in working notes so interrupted research can resume without resubmitting.