القائمة
基于Reddit深度挖掘用户真实痛点和需求。适用于YouTube选题调研、AI产品机会发现、市场需求验证、竞品用户反馈分析、舆情监控、投资调研、技术趋势分析等场景。自动生成多维度搜索、情绪强度评分、结构化报告。
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Leoyishou/personal-ai-company --skill pain-point-researchيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
火山引擎语音识别 - 将音频/视频转文字，支持长音频分段识别。
Use when publishing an Obsidian plugin to the community plugin store, creating GitHub Releases for Obsidian plugins, or submitting PRs to obsidianmd/obsidian-releases
一键部署静态网站到 Vercel 和 Cloudflare Pages。支持 HTML/React/Vue 等任意静态项目。
| name | pain-point-research |
| description | 基于Reddit深度挖掘用户真实痛点和需求。适用于YouTube选题调研、AI产品机会发现、市场需求验证、竞品用户反馈分析、舆情监控、投资调研、技术趋势分析等场景。自动生成多维度搜索、情绪强度评分、结构化报告。 |
| allowed-tools | Bash(python:*), Read, Write, Glob, Grep |
| model | sonnet |
基于 Reddit API 进行多场景深度调研，支持痛点挖掘、舆情分析、投资调研、趋势追踪等。
PAIN_TEMPLATES = {
"emotion_trigger": [
"tired of {topic}",
"frustrated with {topic}",
"why does {topic} suck",
"anyone else hate {topic}",
"sick of {topic}",
],
"desire_seeking": [
"wish there was {topic}",
"how do I {topic}",
"what actually works for {topic}",
"best way to {topic}",
],
"pain_validation": [
"anyone else struggling with {topic}",
"feeling stuck with {topic}",
"can't figure out {topic}",
],
}
SENTIMENT_TEMPLATES = {
"positive": [
"{topic} is amazing",
"love {topic}",
"{topic} changed my life",
"finally {topic} works",
],
"negative": [
"{topic} is terrible",
"hate {topic}",
"{topic} ruined",
"never using {topic} again",
],
"neutral_discussion": [
"thoughts on {topic}",
"what do you think about {topic}",
"{topic} discussion",
"honest opinion {topic}",
],
}
INVESTMENT_TEMPLATES = {
"bullish": [
"{ticker} to the moon",
"buying more {ticker}",
"{ticker} undervalued",
"long {ticker}",
],
"bearish": [
"{ticker} overvalued",
"selling {ticker}",
"{ticker} crash",
"short {ticker}",
],
"analysis": [
"{ticker} DD", # Due Diligence
"{ticker} analysis",
"{ticker} fundamentals",
"is {ticker} worth buying",
],
"sentiment": [
"what happened to {ticker}",
"{ticker} news",
"why is {ticker} down",
"why is {ticker} up",
],
}
TECH_TEMPLATES = {
"comparison": [
"{tech1} vs {tech2}",
"{tech} alternatives",
"switching from {tech}",
"migrating to {tech}",
],
"experience": [
"{tech} in production",
"{tech} real world",
"using {tech} for",
"{tech} experience",
],
"learning": [
"learning {tech}",
"{tech} worth learning",
"{tech} roadmap",
"how long to learn {tech}",
],
}
FINANCE_SUBREDDITS = [
"wallstreetbets", "stocks", "investing", "options",
"cryptocurrency", "Bitcoin", "ethereum", "CryptoMarkets",
"personalfinance", "financialindependence", "Fire",
"Bogleheads", "dividends", "ValueInvesting",
# 中概股/亚洲市场
"ChinaStocks", "Sino",
]
TECH_SUBREDDITS = [
"programming", "webdev", "learnprogramming",
"MachineLearning", "artificial", "LocalLLaMA",
"devops", "sysadmin", "kubernetes",
"reactjs", "node", "golang", "rust",
"technology", "gadgets", "hardware",
]
CAREER_SUBREDDITS = [
"careerguidance", "jobs", "careeradvice",
"cscareerquestions", "ExperiencedDevs",
"antiwork", "workreform", "overemployed",
"Entrepreneur", "startups", "smallbusiness",
]
CONSUMER_SUBREDDITS = [
"BuyItForLife", "Frugal", "deals",
"homeautomation", "smarthome",
"cars", "electricvehicles",
"Apple", "Android", "GooglePixel",
]
NEWS_SUBREDDITS = [
"news", "worldnews", "politics",
"technology", "business", "economics",
"OutOfTheLoop", "explainlikeimfive",
]
询问用户：
根据调研类型选择模板：
| 类型 | 模板 | 核心指标 |
|---|---|---|
| 痛点挖掘 | PAIN_TEMPLATES | 情绪强度、出现频率 |
| 舆情分析 | SENTIMENT_TEMPLATES | 正负比例、情绪趋势 |
| 投资调研 | INVESTMENT_TEMPLATES | 多空比例、关键事件 |
| 技术趋势 | TECH_TEMPLATES | 采用趋势、迁移方向 |
cd /Users/liuyishou/.claude/skills/research-by-reddit/scripts
export $(cat ../.env | grep -v '^#' | xargs)
# 并行执行多个搜索
python analyze_reddit.py \
--query "{搜索词}" \
--search-subreddit {subreddit} \
--search-sort top \
--time-filter month \
--limit 12 \
--include-comments \
--comment-limit 8 \
--analysis-language zh \
--output-md {output}.md
根据调研类型生成不同格式的报告。
# [主题] 痛点调研报告
## 核心发现
| 痛点 | 情绪强度 | 频率 | 产品机会 |
|-----|---------|-----|---------|
## 一级痛点（高需求+高情绪）
### 痛点1: [标题]
**Reddit原话：**
> "..."
**情绪强度：** X/10
**产品/内容机会：** ...
## 金句库
## 行动建议
# [话题/品牌] 舆情分析报告
## 情绪概览
- 正面情绪占比：X%
- 负面情绪占比：X%
- 中性讨论占比：X%
## 关键观点
### 正面评价
### 负面评价
### 争议焦点
## 典型用户声音
## 风险提示
## 建议行动
# [标的] 投资情绪分析
## 情绪指标
- 多空比例：X:Y
- 讨论热度：高/中/低
- 情绪趋势：上升/平稳/下降
## 社区观点
### 看多理由
### 看空理由
### 关键风险
## 近期催化剂
## 散户关注点
## 信息来源质量评估
# [技术/框架] 社区调研
## 采用趋势
- 讨论热度变化
- 新用户 vs 老用户比例
## 使用场景
### 推荐场景
### 不推荐场景
## 优缺点汇总
### 社区认可的优点
### 社区反映的问题
## 替代方案对比
## 学习曲线评估
## 是否值得采用
YouTube/自媒体内容选题调研
AI/SaaS产品机会发现
竞品用户反馈分析
品牌/产品舆情监控
股票/加密货币投资情绪
技术选型/趋势调研
用户自定义调研维度
用户：帮我看看Reddit上对OpenAI的舆情怎么样
Claude：
1. 确认调研类型：舆情分析
2. 选择相关subreddits：ChatGPT, OpenAI, artificial, LocalLLaMA
3. 使用SENTIMENT_TEMPLATES搜索
4. 生成《OpenAI舆情分析报告》
用户：Reddit上对NVIDIA的情绪怎么样
Claude：
1. 确认调研类型：投资情绪
2. 选择相关subreddits：wallstreetbets, stocks, investing, nvda
3. 使用INVESTMENT_TEMPLATES搜索
4. 生成《NVDA投资情绪报告》
用户：调研一下Rust和Go的社区评价
Claude：
1. 确认调研类型：技术对比
2. 选择相关subreddits：rust, golang, programming
3. 使用TECH_TEMPLATES搜索
4. 生成《Rust vs Go 社区调研报告》
用户：Reddit上怎么看DeepSeek
Claude：
1. 确认调研类型：舆情+技术趋势
2. 选择相关subreddits：LocalLLaMA, MachineLearning, artificial
3. 混合使用SENTIMENT + TECH模板
4. 生成《DeepSeek社区反响报告》
| 分数 | 标准 |
|---|---|
| 10 | score>500，评论充满强烈情绪，多人高度共鸣 |
| 8-9 | score>200，明确的情绪倾向，评论活跃 |
| 6-7 | score>100，有情绪但不极端 |
| 4-5 | score<100，存在但不强烈 |
| 1-3 | 低互动，可能是个例 |
本skill依赖 research-by-reddit skill的底层工具：
/Users/liuyishou/.claude/skills/research-by-reddit/scripts/analyze_reddit.py
/Users/liuyishou/.claude/skills/research-by-reddit/.env
需要配置：