SZZG007 Web Deep Research

基于17+平台自动深度调研市场、竞品及客户背景，生成详实报告，支持多角度趋势与风险分析。

Security Scan

The skill's documentation claims cross‑platform web research but the package is instruction‑only and its declared requirements (API keys, local scripts, workspace paths) are missing from the registry metadata — the pieces don't add up and could lead to unexpected data collection or requests for credentials.

Purpose & Capability

The skill claims automated deep research across 17+ platforms (Twitter/X, YouTube, Amazon, Weibo, etc.), which normally requires platform-specific credentials or scraping code. The registry metadata lists no required environment variables or binaries, yet SKILL.md explicitly names TAVILY_API_KEY and AGENT_REACH_API_KEY and references platform-specific scripts. This mismatch (documentation asking for API keys while package declares none) is incoherent.

Instruction Scope

The SKILL.md instructs the agent to perform background checks (including decision‑maker background), aggregate social media and platform data, and shows example inputs like an email address for '背调'. It also references local scripts (research.py, twitter.py, reddit.py) and a cache path. Because the package contains no code, it's unclear how these actions are implemented; the instructions enable potentially sensitive data collection (people background checks) without specifying limits, retention, or where results are sent.

Install Mechanism

There is no install spec (instruction-only), which is lower risk in that nothing is written by an installer. However, SKILL.md describes a workspace with Python scripts that are not present in the package. That inconsistency could lead the agent to attempt to fetch or generate code at runtime or to fail — users should confirm whether supporting code will be provided and from what source.

Credentials

The skill's registry metadata lists no required env vars, but the SKILL.md lists TAVILY_API_KEY and AGENT_REACH_API_KEY (plus configurable concurrency and language). Requesting API keys for search/social APIs is plausible, but the metadata omission is a mismatch. Also, many target platforms (Twitter/X, YouTube, Alibaba, Amazon, Chinese platforms) commonly require additional credentials or scraping capabilities that are not declared — this is disproportionate/unclear.

Persistence & Privilege

always is false and autonomous invocation is allowed (the platform default). The skill does not request system-wide configuration changes or permanent presence beyond typical workspace paths. No explicit privilege escalation or cross-skill modifications are declared.

What to consider before installing

Do not install or supply API keys yet. Ask the publisher for: (1) the full source or packaged code (those referenced Python scripts and templates), (2) an explicit list of required credentials and why each is needed, (3) how personal data (background checks) is collected, stored, and retained (privacy/legality), and (4) whether the agent will fetch code from external URLs at runtime and what those URLs are. If you must test, avoid providing high‑privilege credentials (e.g., social media tokens or company secrets) until these questions are answered and you can review the code or a trusted release. If the author cannot supply the missing code and a clear data‑handling policy, treat the skill as untrusted.

szzg007-web-deep-research - 全网深度调研专家

版本: 1.0
创建日期: 2026-03-30
作者: MOSSRIVER 团队

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技能描述

自主在全网进行深度调研，覆盖 17+ 平台，自动整理调研报告。专为市场情报、竞品分析、客户背调设计。

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支持平台

社交媒体 (8 个)

平台	查询内容
Twitter/X	品牌提及、行业趋势、KOL 动态
Reddit	用户讨论、产品评价、痛点收集
YouTube	产品评测、使用教程、竞品视频
Instagram	视觉内容、网红种草、热门标签
LinkedIn	公司信息、决策人背景、招聘动态
TikTok	病毒内容、年轻用户偏好
小红书	中国用户评价、种草笔记
微博	中国品牌动态、热点话题

商业平台 (5 个)

平台	查询内容
GitHub	开源项目、技术趋势
Product Hunt	新品发布、用户反馈
阿里巴巴	供应商信息、价格对比
亚马逊	产品评价、销量排名
Shopify	独立站分析、竞品店铺

其他 (4 个)

平台	查询内容
微信公众号	行业文章、品牌动态
抖音	短视频内容、直播带货
B 站	长视频评测、用户弹幕
RSS/网页	自定义来源监控

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核心功能

1. 市场调研

• 行业趋势分析
• 市场规模估算
• 增长机会识别
• 风险因素评估

2. 竞品分析

• 竞品产品线梳理
• 价格策略分析
• 营销渠道识别
• 用户评价汇总

3. 客户背调

• 公司信息查询
• 决策人背景调查
• 社交媒体活跃度
• 采购能力评估

4. 趋势监控

• 热门话题追踪
• 新兴品牌发现
• 用户需求变化
• 技术创新动态

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触发词

"帮我调研一下这个市场"
"查一下这个公司的背景"
"全网搜索这个产品的评价"
"竞品分析报告"
"市场趋势分析"
"客户背调"
"szzg007 深度调研"

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使用方法

基础用法

调研一下收纳盒市场

高级用法

调研一下收纳盒市场
- 目标市场：美国
- 关注平台：Amazon, Instagram, TikTok
- 输出格式：Markdown 报告
- 深度：深度 (默认快速/标准/深度)

客户背调

背调这个客户：[e-mail]
- 公司名：ABC Company
- 关注点：采购能力、信用记录

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输出模板

# 市场调研报告

**调研主题:** XXX  
**调研时间:** 2026-03-30  
**调研深度:** 深度  
**覆盖平台:** 12 个

## 执行摘要
- 市场规模：$XXB (2026)
- 增长率：XX% YoY
- 主要玩家：A, B, C
- 关键趋势：1, 2, 3

## 竞品分析

### 竞品 A
- 产品线：XX 款
- 价格区间：$XX-$XX
- 主要渠道：Amazon, Shopify
- 用户评价：4.5/5 (1200 条评价)

### 竞品 B
...

## 用户洞察
- 核心痛点：1, 2, 3
- 购买决策因素：价格、质量、设计
- 热门关键词：XXX, YYY, ZZZ

## 机会识别
1. [机会 1] - 依据
2. [机会 2] - 依据

## 风险因素
1. [风险 1] - 缓解建议
2. [风险 2] - 缓解建议

## 数据来源
- Twitter: XX 条推文
- Reddit: XX 个讨论
- Amazon: XX 条评价
- ...

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配置说明

环境变量

# Tavily API (主要搜索源)
TAVILY_API_KEY=your_key

# Agent-Reach (可选，用于社交媒体)
AGENT_REACH_API_KEY=your_key

# 输出语言
WEB_RESEARCH_LANG=zh-CN

# 并发请求数
WEB_RESEARCH_CONCURRENCY=5

工作空间

~/.openclaw/workspace/skills/szzg007-web-deep-research/
├── SKILL.md
├── templates/          # 报告模板
├── examples/           # 调研案例
├── scripts/
│   ├── research.py     # 主脚本
│   ├── twitter.py      # Twitter 查询
│   ├── reddit.py       # Reddit 查询
│   └── report.py       # 报告生成
└── cache/              # 查询缓存

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示例

示例 1: 市场调研

输入:

调研一下美国收纳盒市场

输出:

# 美国收纳盒市场调研报告

## 执行摘要
- 市场规模：$3.2B (2026)
- 增长率：8.5% YoY
- 主要玩家：The Container Store, IKEA, Amazon Basics
- 关键趋势：彩色设计、可堆叠、环保材质

## 竞品分析
### The Container Store
- 产品线：150+ 款
- 价格区间：$15-$120
- 主要渠道：线下店 + 电商
- 用户评价：4.3/5

### IKEA
- 产品线：80+ 款
- 价格区间：$5-$50
- 主要渠道：线下店 + 电商
- 用户评价：4.1/5

## 用户洞察
- 核心痛点：空间小、难整理、不美观
- 购买决策因素：尺寸匹配、透明度、价格
- 热门关键词："stackable", "clear", "colorful"

## 机会识别
1. 彩色收纳盒 - 市场少见，差异化明显
2. 可堆叠设计 - 用户高度关注
3. 环保材质 - 年轻消费者偏好

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与其他技能协作

技能	协作方式
szzg007-product-analyzer	输入竞品信息 → 卖点分析
szzg007-customer-crm	输入客户名 → 背调报告
szzg007-email-business-manager	输入调研结果 → 营销话术

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此技能专为 MOSSRIVER 跨境电商设计，持续优化中。