主题需求验证

安装指南

1. ClawHub CLI 从源直接安装技能的最快方式。
   npx clawhub@latest install topic-demand-validation
2. 手动安装 将技能文件夹复制到以下位置之一
   全局模式
   ~/.openclaw/skills/
   工作区
   /skills/
   优先级：工作区 > 本地 > 内置
3. 提示词安装 将此提示词复制到 OpenClaw 即可自动安装。
   请帮我使用 Clawhub 安装 topic-demand-validation。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。
   更倾向于下载？
   获取包含原生技能文件的ZIP压缩包。
   下载技能 (.zip)

什么是

主题需求验证可帮助判断某个主题、产品类别、受众问题或内容创意是否值得进一步测试。它将 Google Suggest、Google Trends、Bing 或 Brave 搜索结果，以及来自 Reddit、TikTok 或 YouTube 的精选样本整合成一份简明的证据摘要。
此技能会将不同类型的信号分开处理，而不是编造误导性的需求评分。它会区分查询措辞、相对趋势数据、排名竞争情况和观察到的公开讨论，帮助团队做出更有依据的决策，同时避免根据有限样本声称销售额、市场规模、受众覆盖范围或未来增长情况。

应用场景

评估新的产品类别或内容主题是否存在可见需求。
了解目标受众在搜索时如何表述问题或意图。
在一致的 Google Trends 环境中比较最多五个相关词项。
识别具有代表性的 SERP 竞争者、域名和结果格式。
抽样查看 Reddit、TikTok 或 YouTube 讨论，了解反复出现的需求和主题。
决定是否应进一步探索、测试或暂缓某个想法。
在明确记录覆盖范围缺口的同时，找到一个具体的下一步验证措施。
研究类别或问题，而非已知品牌；针对特定实体的声誉或提及分析，请使用 brand-mention-research。

工作原理

确定范围：主题、产品或内容决策、目标市场、语言、地理区域、时间范围、来源和排除项。如果缺少细节，请说明所选默认值。
使用 Google Suggest 扩展种子词组，并保留确切前缀、国家/地区、语言、返回的补全词项和收集时间。
在一次 Google Trends 请求中比较不超过五个选定词项，并统一地理区域、日期范围、类别、搜索类型和区域设置。记录缺失数据或零数据状态，但不要将其视为零需求。
针对重要查询簇检查有限范围的 Bing 和/或 Brave SERP 样本，并记录确切查询、排名、标题、URL、结果类型和收集背景。
使用聚焦查询搜索相关的 Reddit、TikTok 或 YouTube 来源。保留筛选条件、分页信息、结果 ID、日期和相关结果数量。
仅打开选定的相关帖子或视频。如果评论、说明文字或文字稿有助于阐明表达出的需求，则使用这些内容；同时对结果去重，并将顶层帖子与评论区分开。
将发现归类为相关、模糊或不相关，并根据实际文本识别主题，而不是依据帖子数、观看次数或评论数推断受众规模。
用文字综合支持证据、反对证据、来源覆盖范围限制和未解决的问题。不要将不同类型的指标合并成数值需求评分。
提交一份简明摘要，涵盖范围、意图、趋势变化、竞争情况、讨论证据和具体的下一步验证措施。
保守地处理故障：遇到身份验证错误时停止；对 429 响应遵守 Retry-After；可重试的 503 最多重试一次；并将空样本或不相关样本报告为“在此样本中未验证”。

配置指南

确保已提供并可执行捆绑的 scripts/crawlora.sh 辅助脚本。
配置 CRAWLORA_API_KEY 环境变量。辅助脚本会将其作为请求标头发送，不会将密钥放入 URL。
查看 reference/endpoints.md，了解支持的端点、HTTP 方法、参数和请求结构。
从适度的采集预算开始：少量自动补全前缀、一次最多比较五个词项的 Google Trends 查询、每个重要查询簇一到两个 SERP 查询，以及社交结果首页的有限样本。
export CRAWLORA_API_KEY="your-api-key"
chmod +x scripts/crawlora.sh
采集命令示例：
scripts/crawlora.sh /google/suggest q="meal prep for shift workers" country=us lang=en count=10
scripts/crawlora.sh -X POST /google/trends/explore/interest-over-time '{"keywords":["meal prep for shift workers","night shift meal prep"],"geo":"US","time_range":"today 12-m","type":"web"}'
scripts/crawlora.sh /bing/search q="meal prep for shift workers" count=10 country=us lang=en
scripts/crawlora.sh /reddit/search q="meal prep for shift workers" sort=top time=year limit=10
scripts/crawlora.sh /tiktok/search keyword="meal prep for shift workers" count=10
scripts/crawlora.sh /youtube/search q="meal prep for shift workers" type=video sort_by=relevance
在增加搜索数量或深入分页之前，应先就更大的采集预算达成一致，因为成功的 API 调用可能会产生费用。

数据架构

此技能会将证据整理成一份简明的研究摘要，其中包含彼此独立的部分和明确的采集元数据。
部分 必需数据
范围 主题、决策、目标市场、地理区域、语言、日期范围、纳入或排除的来源、查询列表和抽样限制
搜索意图 自动补全前缀、返回的建议词、收集时间、区域设置，以及推断出的词组或意图簇
搜索变化 对比的 Trends 词项、地理区域、范围、类别、搜索类型、区域设置、相对时间线、上升查询、相关主题和缺失数据状态
搜索竞争 确切查询、搜索引擎、国家/地区或语言、排名、标题、URL、域名、结果类型和收集时间
讨论样本 平台、聚焦查询、筛选条件、分页信息、结果 ID、日期、相关性标签、链接、主题、评论、说明文字或文字稿证据
综合分析 支持证据、反对证据、来源覆盖范围限制、未解决的问题和下一步验证措施

数据架构

自动补全：候选措辞和意图，不是搜索量估算。
Trends：同一一致请求中的标准化抽样相对兴趣度，不是查询次数。
SERP：受时间和地点限制的排名证据，不是全面的竞争或市场需求情况。
社交讨论：归类为相关、模糊或不相关的观察示例，不代表独立覆盖人数或受众总规模。
状态处理：保留 Trends 的零数据或缺失数据，并将空搜索、404、失败或大多不相关的搜索标记为“在此样本中未验证”。
证据边界：将测量所得的观察结果与假设分开，绝不创建综合数值需求评分。

高级特性

对 Google Suggest、Google Trends、Bing 或 Brave SERP、Reddit、TikTok 和 YouTube 进行跨来源交叉验证。
一致的多词项 Trends 比较，每次请求最多五个词项。
查询簇分析，用于区分受众问题、产品类别和内容意图。
有限抽样控制，以限制初始 API 开支并支持有计划地扩展。
当平台级日期筛选不可用时，对 TikTok 结果进行本地日期筛选。
根据平台特性解读 Reddit 时间筛选、YouTube 上传日期筛选、分页和选定结果的限制。
通过定向查看评论、说明文字或视频文字稿补充背景信息。
对结果去重，并明确区分顶层帖子与评论。
具备故障处理能力的采集机制：遵守 Retry-After、限制重试次数、遇到身份验证错误时停止，并避免绕过挑战或轮换凭据。
证据摘要会展示反向信号、覆盖缺口和可测试的下一步行动，而不会夸大商业验证结论。
通过捆绑的
scripts/crawlora.sh
辅助脚本和请求标头安全处理 API 密钥。

name: topic-demand-validation description: Validate a topic, product category, or content idea by comparing autocomplete intent, Google Trends movement, search-result competition, and a bounded sample of Reddit, TikTok, or YouTube discussion. Use for questions like “is there demand for this?”, “what topic should we test next?”, or “what are people searching and discussing?”; use brand-mention-research for a known entity’s mentions.

Topic demand validation

Build an evidence brief about what people search for, what is changing, what already ranks, and what a bounded public discussion sample says. Keep those signals separate: they answer different questions and do not establish sales, market size, or future growth.

Scope and inputs

Ask for or infer the topic, product/content decision, target market and language, time window, and any excluded sources. If geography or period is unspecified, state the chosen defaults before collecting. Keep the scope narrow enough to compare consistently.

Use this workflow for category, problem, audience, or content-topic discovery. For one query’s autocomplete or SERP, use the focused SERP workflow. For a named brand’s mentions and reputation, use brand-mention-research.

Collect comparable evidence

Expand one or a few seed phrases with Google Suggest. Save the exact prefix, country, language, returned completions, and collection time. Suggestions reveal query phrasing; they are not search-volume estimates.
Compare no more than five selected terms in one Google Trends request. Keep geography, range, category, search type, and locale consistent. Use rising queries or related topics to discover candidates, then compare those candidates in a shared request. If a term has zero or missing data, preserve that status; do not treat it as zero demand.
Inspect a bounded SERP sample with Bing and/or Brave for the highest-value query clusters. Record each exact query, country/language, rank, title, URL, and result type. Search results show what surfaced for that query at collection time, not the size of the market.
Sample one or more relevant social sources—Reddit, TikTok, or YouTube—using focused queries. Record the query, filters, time, pagination, result IDs, dates, and how many sampled results are actually relevant. Reddit's time filter applies to top/comments sorts; do not claim that new plus a time value enforces a date window. TikTok search has no date filter here: filter returned dates locally and mark unknown dates. YouTube offers upload-date filters, but results remain a selected sample. Search feeds may be noisy or incomplete; narrow a query or community when needed, and stop when the sample cannot support the question.
Open only selected relevant posts/videos for context. Read comments or captions/transcripts when they materially clarify the expressed need. Deduplicate within each platform and distinguish top-level posts from comments. Do not turn sampled posts, views, or comment counts into unique audience reach.

Use a modest first pass when the user has not set a budget: one Trends comparison of up to five terms, a small set of autocomplete prefixes, one or two SERP queries per important cluster, and a bounded first page of social results. Expand only when the initial evidence is relevant and the decision needs it. Successful API calls are billable; agree a wider collection budget with the user before multiplying searches or paging deeply.

Interpret without blending unlike metrics

Treat autocomplete as candidate phrasing and intent. Google says predictions reflect real searches but also factors such as language, location, and current interest; they are not a simple popularity list.
Treat Trends as a sampled, normalized relative index for the selected query context. Compare terms in the same request. Do not compare independently scaled series as if they share a numeric scale, or translate index values into query counts.
Treat SERP results as a time- and location-bound sample of what ranks, not exhaustive competition or demand.
Treat social results as observed examples. Label relevant, ambiguous, and irrelevant matches; classify themes from the actual post/comment text. A failed or irrelevant search is a coverage limit, not proof that no one is discussing the topic.
Do not invent a combined numeric “demand score.” Synthesize support, counter-signals, source coverage, and unresolved questions in words. Separate measured evidence from a testable hypothesis.

Deliverable

Return a compact brief with:

Scope: topic, market/language, date window, sources, query list, and sampling limits.
Search intent: autocomplete phrase clusters and the intent each suggests.
Search movement: Trends terms, settings, relative timeline, and rising/related queries where available.
Search competition: representative ranked results, domains, and result formats.
Discussion sample: relevant/ambiguous/irrelevant counts, themes, dates, links, and any sampled comments or transcript evidence.
Synthesis: evidence for, evidence against, missing coverage, and one concrete next validation step. State whether the evidence supports exploring, testing, or holding the idea; do not claim commercial validation from these signals alone.

Failure handling

Check application code as well as HTTP status. Stop on authentication errors. For a 429 or retryable 503, honor Retry-After and retry once at most; do not rotate credentials, fan out repeated requests, or bypass a challenge. If a source returns an empty/404 result or mostly irrelevant content, report the search and its limit as “not validated in this sample,” not “no demand.”

Examples

scripts/crawlora.sh /google/suggest q="meal prep for shift workers" country=us lang=en count=10
scripts/crawlora.sh -X POST /google/trends/explore/interest-over-time '{"keywords":["meal prep for shift workers","night shift meal prep"],"geo":"US","time_range":"today 12-m","type":"web"}'
scripts/crawlora.sh /bing/search q="meal prep for shift workers" count=10 country=us lang=en
scripts/crawlora.sh /reddit/search q="meal prep for shift workers" sort=top time=year limit=10
scripts/crawlora.sh /tiktok/search keyword="meal prep for shift workers" count=10
scripts/crawlora.sh /youtube/search q="meal prep for shift workers" type=video sort_by=relevance

Read reference/endpoints.md for the selected endpoints, exact methods, parameters, and request shapes. Use the bundled scripts/crawlora.sh helper; it sends CRAWLORA_API_KEY as a header and does not put the key in the URL.

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