SocQ LinkedIn Research

调用 SocQ 的 MCP、CLI 或 REST 接口检索、采集和导出公开 LinkedIn 内容、账号、关键词与表现数据；负责端点选择、输入校验、额度估算、异步任务轮询、分页与错误处理，并在付费批量运行前请求用户确认。

热度 64.28

最近更新 2026-10-16 10:09:06

分类：数据依赖：代码执行 / 第三方 API / MCP 服务应用场景：研究 / 数据处理 / API 集成

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add socq-linkedin-research

Metadata

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"]}]}}

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

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.

Read 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.

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.

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

Include:

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.