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Install in OpenClaw
/install consumer-research
Description
消费者调研能力；支持目标人群画像分析、核心需求挖掘、问卷设计与数据处理；当需要进行消费者洞察、产品定位或用户研究时使用
README (SKILL.md)
消费者调研
任务目标
- 本技能用于：了解目标消费者特征、需求与偏好，指导产品定位
- 核心能力：人群画像、需求分析、问卷调研
- 触发条件：新品开发立项、用户研究、产品优化方向确定
数据来源
在线数据源
| 来源 | 网址 | 内容 |
|---|---|---|
| CBNData | https://www.cbndata.com | 消费人群画像、行业趋势 |
| QuestMobile | https://www.questmobile.com | 移动互联网用户洞察 |
| TalkingData | https://www.talkingdata.com | 消费者行为分析 |
| 艾瑞咨询 | https://www.iresearch.cn | 用户研究报告 |
数据获取方式
使用 web_search 搜索消费者洞察：
# 人群特征研究
web_search(query="健身人群 消费者画像 行为特征")
web_search(query="Z世代 食品消费 偏好 趋势")
# 需求洞察
web_search(query="减脂人群 食品需求 痛点")
web_search(query="老年人 健康食品 消费趋势")
# 市场报告
web_search(query="2024年 食品消费者洞察 报告")
web_search(query="无糖食品 消费者接受度 调研")
数据存储
采集的数据保存在工作区 data/consumer_data/ 目录：
profile_data.json- 人群画像数据needs_analysis.json- 需求分析数据questionnaire_results.csv- 问卷调查结果
操作步骤
1. 确定目标人群
- 使用
web_search搜索目标人群特征 - 分析人口统计特征（年龄、性别、收入、地域）
- 分析心理特征（价值观、生活方式）
2. 挖掘核心需求
- 整理目标人群的核心痛点
- 搜索相关调研报告获取数据支撑
- 识别高机会需求
3. 设计调研问卷
- 根据人群画像设计问卷结构
- 参考问卷模板（见附件）
- 确定调研渠道和样本量
4. 数据分析与输出
- 收集问卷数据
- 生成人群画像报告
- 输出产品定位建议
资源索引
- 调研脚本：见 scripts/consumer_research.py
- 问卷模板：见 assets/questionnaire_template.md
- 调研规范：见 references/research_guidelines.md
注意事项
- 保护消费者隐私数据
- 样本量需满足统计显著性
- 问卷设计需遵循专业规范
Usage Guidance
This skill is coherent with consumer research work: it generates search queries, saves profile/needs data under data/consumer_data/, and can produce a report and questionnaire template. Before installing, consider: 1) The SKILL.md names pandas but the script does not use it — verify whether your environment needs to install pandas or whether the dependency is extraneous. 2) The skill writes survey data to workspace files; do not feed it real personal data (PII) unless you confirm storage, retention, and consent/compliance requirements. 3) Review the included script if you will run it in your environment (it currently only prints queries and reads/writes JSON/MD). 4) If you expect automatic network activity, note the skill relies on a 'web_search' tool (the skill itself does not perform HTTP requests). If any file or behavior seems unexpected, inspect or run in an isolated environment first.
Capability Assessment
Purpose & Capability
The skill's name, description, included files (questionnaire template, research guidelines, a script that generates web_search queries and saves JSON reports) and runtime instructions are coherent with consumer research. However, SKILL.md lists a python dependency (pandas==1.5.0) even though the shipped script does not import or use pandas; registry-level metadata lists no required binaries/env. This is likely an unnecessary/leftover dependency and should be cleaned up, but it does not indicate intent to do something unrelated to the stated purpose.
Instruction Scope
Runtime instructions restrict actions to performing web searches (via a web_search tool), designing questionnaires, collecting/storing survey results into data/consumer_data/, and generating reports. The script only formats search queries and reads/writes local JSON/MD files. There are no instructions to read arbitrary system files, access credentials, or transmit data to unexpected external endpoints.
Install Mechanism
There is no install spec in the registry (instruction-only), which is low risk. SKILL.md names a python dependency (pandas==1.5.0) under dependency but no install mechanism is provided; additionally the included Python script does not use pandas. This mismatch is a packaging/information inconsistency — not an immediate security risk but it may confuse users about required runtime packages.
Credentials
The skill requests no environment variables, no credentials, and no config paths. The code writes only to a local workspace directory (data/consumer_data). There are no requests for unrelated secrets or access to external service tokens, which is proportionate to the stated task.
Persistence & Privilege
The skill is not set to always:true and does not request elevated platform privileges. It stores its own outputs under data/consumer_data/ only and does not attempt to modify other skills or global agent configuration. Autonomous invocation is permitted by default but is not combined with other red flags.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install consumer-research - After installation, invoke the skill by name or use
/consumer-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Consumer research skill initial release.
- Added core files for consumer insight tasks: questionnaire template, research guidelines, and data analysis script.
- Replaced audit and regulatory reference documents with consumer research materials.
- Supports user research, target audience profiling, and needs analysis.
- Includes resources for questionnaire creation, data collection, and reporting.
- Data storage strategy and main analysis steps documented for user guidance.
Metadata
Frequently Asked Questions
What is consumerResearch消费者人群画像分析?
消费者调研能力；支持目标人群画像分析、核心需求挖掘、问卷设计与数据处理；当需要进行消费者洞察、产品定位或用户研究时使用. It is an AI Agent Skill for Claude Code / OpenClaw, with 148 downloads so far.
How do I install consumerResearch消费者人群画像分析?
Run "/install consumer-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is consumerResearch消费者人群画像分析 free?
Yes, consumerResearch消费者人群画像分析 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does consumerResearch消费者人群画像分析 support?
consumerResearch消费者人群画像分析 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created consumerResearch消费者人群画像分析?
It is built and maintained by Tinker (@cloudyxuq); the current version is v1.0.0.
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