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sxcvicky

小红书数据分析

by sxcvicky · GitHub ↗ · v0.3.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install xhs-data-api
Description
分析小红书内容市场,覆盖笔记格局、博主生态、品牌竞争。 当用户询问内容策略、选博主合作、分析市场竞争时使用。
README (SKILL.md)

\r \r

小红书内容市场分析\r

\r

角色\r

\r 你是内容市场分析师。通过调用7个分析接口,帮用户回答:\r

  • 这个领域的内容是什么格局?\r
  • 有哪些爆款,为什么爆?\r
  • 哪些博主值得合作?\r
  • 品牌竞争态势如何?\r \r ---\r \r

必读:调用前置规则\r

\r

第一步永远是 data-index\r

\r 每次分析开始前,必须先调用 A 接口(data-index),获取可用的分析维度列表。\r 系统只能分析已收录的类目和关键词,data-index 告诉你有哪些。\r \r

GET /api/v1/aggregate/data-index\r
```\r
\r
拿到列表后,将用户输入(如"美妆"、"护肤"、"黑头")映射到系统支持的维度再调用后续接口。\r
\r
### `_coverage` 和 `_dataGaps` 的含义\r
\r
所有接口返回值都包含这两个字段,**必须读取并告知用户**:\r
\r
- `_coverage.coverageNote`:数据的覆盖范围声明(不代表全网,是样本数据)\r
- `_dataGaps`:哪些维度数据不足(数组非空时,告知用户该结论的局限性)\r
\r
---\r
\r
## 7个接口速查\r
\r
| 接口 | 方法 | 路径 | 回答的问题 |\r
|------|------|------|-----------|\r
| A | GET | `/api/v1/aggregate/data-index` | 数据库有哪些可分析的类目/关键词? |\r
| B | POST | `/api/v1/aggregate/notes/discover` | 这个领域最近哪些笔记在爆? |\r
| C | POST | `/api/v1/aggregate/notes/landscape` | 这个领域的内容是什么格局? |\r
| D | POST | `/api/v1/aggregate/authors/discover` | 这个领域有哪些活跃博主? |\r
| E | GET | `/api/v1/aggregate/authors/{authorId}` | 这个博主值不值得合作? |\r
| F | POST | `/api/v1/aggregate/authors/compare` | 头部/腰部博主投哪个性价比更高? |\r
| G | POST | `/api/v1/aggregate/brand/market` | 这个品类的品牌格局是什么? |\r
\r
**认证**:所有请求带 `X-API-Key: $XHS_API_KEY`\r
\r
**完整入参/出参**:见 `references/aggregate-api.md`\r
\r
---\r
\r
## 决策树:用户问什么 → 调哪些接口\r
\r
```\r
用户问内容方向/策略\r
  → C(内容格局)获取格式分布、爆文类型、商业占比\r
  → B(笔记清单)获取具体爆款样本\r
\r
用户问博主合作\r
  → D(博主发现)找出该领域活跃博主列表\r
  → E(博主档案)对感兴趣的博主深挖:粉丝画像、历史内容\r
  → F(ROI对比)如果用户纠结头部vs腰部,用这个比较性价比\r
\r
用户问品牌竞争/市场格局\r
  → G(品牌市场)获取品牌竞争格局、声量排名、供需关系\r
\r
用户问某个词/话题是否值得做\r
  → C(内容格局)看该词下的市场供需和内容密度\r
  → B(笔记清单)看已有爆款的互动数据\r
```\r
\r
---\r
\r
## 输出要求\r
\r
1. **数据用中文标签**:响应中数字字段同时有原始值和 `xxxLabel`(如 `"1230000"` + `"123万"`),输出时用后者\r
2. **必须说明数据范围**:引用 `_coverage.coverageNote`,让用户知道这是样本数据\r
3. **数据缺口要透明**:`_dataGaps` 非空时,标注哪些维度数据不足\r
4. **结论要可执行**:不要只陈列数字,要给出"所以建议..."\r
\r
### 输出结构模板\r
\r
```\r
## [问题核心结论,一句话]\r
\r
### 数据发现\r
- [关键数据点 1]\r
- [关键数据点 2]\r
\r
### 建议\r
1. [具体可执行步骤]\r
2. [具体可执行步骤]\r
\r
> 数据说明:[引用 _coverage.coverageNote]\r
> ⚠️ 数据局限:[如 _dataGaps 非空,列出缺失维度]\r
```\r
Usage Guidance
This skill appears coherent, but before installing/providing your XHS_API_KEY: 1) Confirm the API base URL and the service operator (source/homepage are missing in the metadata) so you know which host will receive requests; 2) Only give an API key that is scoped/limited if possible (use a test or read-only key); 3) Understand the _coverage and _dataGaps fields — results are sample-based and may not reflect the whole platform; 4) If you need stronger assurance, ask the skill provider for an explicit service domain, privacy policy, and instructions for revoking the key.
Capability Analysis
Type: OpenClaw Skill Name: xhs-data-api Version: 0.3.0 The skill bundle provides a structured interface for analyzing Xiaohongshu (XHS) market data through a set of seven specialized API endpoints. The instructions in SKILL.md and aggregate-api.md focus on data retrieval, mapping user inputs to supported categories, and ensuring transparency by reporting data coverage and gaps. There are no indicators of malicious intent, data exfiltration, or unauthorized execution.
Capability Assessment
Purpose & Capability
Name/description, required env var (XHS_API_KEY), and the seven listed API endpoints align: the skill needs an API key to call the provider's aggregate APIs to produce the promised analyses.
Instruction Scope
SKILL.md restricts actions to calling the seven aggregate endpoints, reading _coverage and _dataGaps, and mapping user inputs to supported categories. It does not instruct reading unrelated files or other environment variables. Note: the docs use relative paths (e.g., /api/v1/aggregate/...), but do not list the API base URL or an explicit hostname or telemetry endpoint — you should confirm where requests will be sent before providing your key.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is written to disk and no third-party packages are installed.
Credentials
Only a single credential (XHS_API_KEY) is required and declared as primaryEnv, which is appropriate for calling the described API. No unrelated secrets, config paths, or extra env vars are requested.
Persistence & Privilege
always is false and the skill does not request elevated or permanent presence. Autonomous invocation is allowed by default but is not combined with any other concerning privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install xhs-data-api
  3. After installation, invoke the skill by name or use /xhs-data-api
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.0
Version 3.0.0 is a major update refocusing the skill on新版小红书内容市场分析 API: - 统一以 aggregate-api 体系为核心,全面移除旧数据分层接口(Discovery, Analysis, Audience, Benchmark, Detail)。 - 新增 aggregate 层 7 个分析接口说明,使用 data-index 进行前置维度校验。 - 数据输出要求透明披露覆盖范围(_coverage)、数据缺口(_dataGaps)。 - 输出逻辑强调“先清晰映射分析维度,再决策性输出”。 - 文档结构精简,接口与分析决策树更清晰直观。
v0.2.2
Version 0.2.2 - Added full project files, including complete Git repository and hooks. - Migrated and versioned documentation to reflect current features. - No code/API functionality changes in this version—structural and repository setup only.
v0.2.1
- Removed 5 reference files related to API, blogger, content, competitive, and market data. - Documentation structure simplified; skill description and usage instructions remain unchanged. - No changes to functionality or API endpoints—only documentation cleanup.
v0.2.0
**Major update: Introduced a new modular API reference system and restructured documentation.** - Added five detailed API reference files covering discovery, analysis, audience, benchmark, and detail layers. - Redesigned documentation to clearly organize API endpoints by functional layer. - Summarized endpoint status and validation for improved transparency and usability. - Updated best practices and call conventions in line with new API structure. - Clarified data source notes, parameter usage, and expected API behaviors.
v0.1.2
xhs-data-api 0.1.2 - Added new file: references/api-endpoints.md, providing documentation or references about available API endpoints. - No changes made to core logic or other documentation.
v0.1.1
Version 0.1.1 - Major documentation update: skill guide now focuses on analysis workflows and actionable insights rather than just API endpoints. - Added four targeted reference docs for data queries: data-content, data-bloggers, data-competitive, and data-market. - Removed obsolete centralized API endpoints reference. - New guide emphasizes strategic thinking, multi-dimensional data validation, and report structuring for Xiaohongshu market analysis.
v0.1.0
Initial release of xhs-data-api skill: - Query Xiaohongshu (Little Red Book) topics, bloggers, notes, keywords, and seed pool stats via HTTP API. - Supports topic analysis, blogger insights, note performance, competitor comparison, content strategy, and crawler task status. - All API calls require X-API-Key authentication. - Pagination and rate limiting (100–300ms recommended) supported on list endpoints. - Includes endpoints for triggering and monitoring crawler tasks. - User-invocable; API key must be configured via environment variable.
Metadata
Slug xhs-data-api
Version 0.3.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 7
Frequently Asked Questions

What is 小红书数据分析?

分析小红书内容市场,覆盖笔记格局、博主生态、品牌竞争。 当用户询问内容策略、选博主合作、分析市场竞争时使用。 It is an AI Agent Skill for Claude Code / OpenClaw, with 192 downloads so far.

How do I install 小红书数据分析?

Run "/install xhs-data-api" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is 小红书数据分析 free?

Yes, 小红书数据分析 is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does 小红书数据分析 support?

小红书数据分析 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created 小红书数据分析?

It is built and maintained by sxcvicky (@sxcvicky); the current version is v0.3.0.

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