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SocQ TikTok Research

该 Skill 帮助代理选择并执行 SocQ 的公开 TikTok 研究端点，覆盖歌曲榜、搜索、直播等场景，负责输入校验、信用点预估、异步任务提交与轮询、分页取数以及原始导出与结果汇报，并提供认证、计费、错误处理等参考说明。

socq
热度 64.24
最近更新 2026-10-16 10:13:10
分类：搜索依赖：第三方 API / MCP 服务 / 代码执行 / 读写文件应用场景：研究 / 社交媒体 / 数据处理 / 市场分析

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-tiktok-research
Metadata
name
socq-tiktok-research
description
Research public TikTok content, accounts, keywords, and performance data with SocQ. Use when an agent needs TikTok-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 TikTok Research

SocQ Links

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

Use When

  • The user asks to discover, collect, compare, or analyze public TikTok data.
  • The workflow needs TikTok-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.

Plan TikTok discovery

  • Use tiktok/popular-songs for the TikTok app sound charts: chart_type: "top" selects Top 50 (the default), and chart_type: "viral" selects Viral 50. results_limit defaults to 20 and accepts 1–50. The returned id is a sound clip ID suitable for tiktok/song and tiktok/song-videos.
  • Use tiktok/top-search when both videos and photo carousels are relevant. Region affects content availability and does not guarantee that posts originate there.
  • Use tiktok/user-live with usernames for current live status. A successful offline lookup is one result and costs 0.5 credits; tiktok/live-room-info instead requires room and user IDs.

Endpoint Selection

  • 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.

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=tiktok; use the typed tool listed in 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, keep SOCQ_API_KEY in the environment, and never put it in prompts, URLs, committed files, or retained commands.
  4. Read 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 until succeeded or failed.
  7. Follow pagination.md for all requested pages. Retrieve task files when complete raw JSONL output is required.
  8. Read 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.