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Agent Skill · 研究检索

reddit-insights(红迪网见解)

reddit-insights 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

reddit-insights 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 696。

总安装 195,808周安装 8,411GitHub Stars 4下载 68,634核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.2.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reddit-insights(红迪网见解)
来源仓库:https://github.com/dowands/reddit-insights
安装命令:
openclaw skills install reddit-insights
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install reddit-insights

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词快速定位候选结果时使用。通过 reddapi.dev HTTP API 使用语义 AI 搜索分析 Reddit 内容。适用于发现用户痛点和挫败感,支持市场调研需求。

安装命令为 openclaw skills install reddit-insights,需确认权限范围和维护状态。使用前请核实是否会触发联网、命令执行或文件读写等敏感操作。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install reddit-insights;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

reddit-insights 怎么安装?

在终端执行 openclaw skills install reddit-insights。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

reddit-insights 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

reddit-insights 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

reddit-insights 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

reddit-insights 用的人多吗?

公开数据是累计安装 195,808 次,近一周安装 8,411 次,下载 68,634 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

reddit-insights 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
reddit-insights

红迪网见解 · 本页

195,8084接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
reddit-insights
description
|
Triggers
reddit search, find pain points, market research, user feedback, what do people think about, reddit trends, niche discovery, product validation.

Reddit Insights

Semantic search across millions of Reddit posts. Unlike keyword search, this understands intent and meaning.

Powered by reddapi.dev — AI-powered semantic search and vector similarity across 1000+ subreddits with millions of indexed posts, updated continuously.

Key Advantage:

  • ✅ Two search modes - Semantic (AI summary) + Vector (fast similarity)
  • ✅ Full Reddit archive - Access historical and real-time discussions
  • ✅ AI summaries - Semantic search generates comprehensive summaries
  • ✅ MCP support - Direct integration with Claude Desktop, Cursor, etc.

Setup

Get API Key

  1. Create an account at https://reddapi.dev
  2. Subscribe to a paid plan (Lite $9.90/mo, Starter $49/mo, Pro $99/mo, or Enterprise)
  3. Go to https://reddapi.dev/account to view or generate your API key

Environment Variable

export REDDAPI_API_KEY="your_api_key"

Rate Limits

PlanMonthly API CallsPer Minute
Lite50050
Starter5,00050
Pro15,000100
EnterpriseUnlimited1,000

HTTP API Reference

Base URL: https://reddapi.dev

Authentication: All requests require header:

Authorization: Bearer YOUR_API_KEY

POST /api/v1/search/semantic

AI-powered semantic search with keyword extraction, vector search, and AI summary generation.

curl -X POST "https://reddapi.dev/api/v1/search/semantic" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "What do developers think about Rust vs Go for backend services?", "limit": 20}'
ParameterTypeRequiredDescription
querystringYesNatural language question
limitnumberNoResults to return (default: 20, max: 100)

Response:

{
  "success": true,
  "data": {
    "query": "What do developers think about Rust vs Go for backend services?",
    "results": [
      {
        "id": "1abc234",
        "title": "Switched our microservices from Go to Rust - here's what happened",
        "content": "After 6 months of running Go in production...",
        "subreddit": "rust",
        "upvotes": 847,
        "comments": 234,
        "created": "2026-02-15T10:30:00.000Z",
        "relevance": 0.92,
        "sentiment": "Discussion",
        "url": "https://reddit.com/r/rust/comments/1abc234"
      }
    ],
    "total": 20,
    "processing_time_ms": 12450,
    "ai_summary": "Developers are divided on Rust vs Go for backend services..."
  }
}

POST /api/v1/search/vector

Fast vector similarity search. No LLM processing, returns results in seconds.

curl -X POST "https://reddapi.dev/api/v1/search/vector" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "best productivity apps for ADHD", "limit": 30, "start_date": "2026-01-01", "end_date": "2026-03-18"}'
ParameterTypeRequiredDescription
querystringYesSearch query
limitnumberNoResults to return (default: 30, max: 30)
start_datestringNoStart date filter (YYYY-MM-DD)
end_datestringNoEnd date filter (YYYY-MM-DD)

Response:

{
  "success": true,
  "data": {
    "query": "best productivity apps for ADHD",
    "results": [
      {
        "id": "2def567",
        "title": "Finally found an app that works for my ADHD brain",
        "content": "I've tried everything from Todoist to Notion...",
        "subreddit": "ADHD",
        "upvotes": 1203,
        "comments": 456,
        "created": "2026-03-01T14:22:00.000Z",
        "similarity_score": 0.89,
        "url": "https://reddit.com/r/ADHD/comments/2def567"
      }
    ],
    "total": 30,
    "processing_time_ms": 3200
  }
}

GET /api/v1/subreddits

List available subreddits with metadata, sorted by subscribers.

curl "https://reddapi.dev/api/v1/subreddits?search=programming&limit=10" \
  -H "Authorization: Bearer YOUR_API_KEY"
ParameterTypeRequiredDescription
searchstringNoFilter by name/title/description
pagenumberNoPage number (default: 1)
limitnumberNoResults per page (default: 50, max: 100)
sortstringNoSort by "subscribers" or "created" (default: subscribers)
orderstringNo"asc" or "desc" (default: desc)

GET /api/v1/subreddits/{name}

Get detailed information about a specific subreddit with recent posts.

curl "https://reddapi.dev/api/v1/subreddits/webdev" \
  -H "Authorization: Bearer YOUR_API_KEY"
ParameterTypeRequiredDescription
namestringYesSubreddit name without r/ prefix

POST /api/v1/trends

Get trending topics from Reddit based on post engagement.

curl -X POST "https://reddapi.dev/api/v1/trends" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"start_date": "2026-03-11", "end_date": "2026-03-18", "limit": 20}'
ParameterTypeRequiredDescription
start_datestringNoStart date (default: today)
end_datestringNoEnd date (default: today)
limitnumberNoTopics to return (default: 20, max: 100)

MCP Server

reddapi.dev also provides a Model Context Protocol (MCP) server for direct integration with AI clients.

Endpoint: https://reddapi.dev/api/mcp Protocol: MCP Streamable HTTP Transport

{
  "mcpServers": {
    "reddit-search-api": {
      "transport": {
        "type": "http",
        "url": "https://reddapi.dev/api/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_KEY"
        }
      }
    }
  }
}

MCP tools: reddit_semantic_search, reddit_vector_search, reddit_list_subreddits, reddit_get_subreddit, reddit_get_trends


Choosing Between Semantic and Vector Search

Use CaseRecommendedWhy
Exploratory researchSemanticLLM extracts keywords, generates summary
Known-topic monitoringVectorFaster, direct matching, no LLM overhead
Batch processingVector5x faster per request
Answering complex questionsSemanticAI summary synthesizes insights
Real-time dashboardsVectorLow latency (~5s vs ~20s)

Best Use Cases (Tested)

Use CaseEffectivenessWhy
Product comparisons (A vs B)⭐⭐⭐⭐⭐Reddit loves debates
Tool/app recommendations⭐⭐⭐⭐⭐High-intent discussions
Side hustle/money topics⭐⭐⭐⭐⭐Engaged communities
Pain point discovery⭐⭐⭐⭐Emotional posts rank well
Health questions⭐⭐⭐⭐Active health subreddits
Technical how-to⭐⭐⭐Better to search specific subreddits
Abstract market research⭐⭐Too vague for semantic search
Non-English queries⭐Reddit is English-dominant

Query Strategies

✅ Excellent Queries (relevance 0.70+)

Product Comparisons (best results!):

"Notion vs Obsidian for note taking which one should I use"
→ Relevance: 0.72-0.81 | Found: Detailed comparison discussions, user experiences

"why I switched from Salesforce to HubSpot honest experience"  
→ Relevance: 0.70-0.73 | Found: Migration stories, feature comparisons

Side Hustle/Money Topics:

"side hustle ideas that actually make money not scams"
→ Relevance: 0.70-0.77 | Found: Real experiences, specific suggestions

✅ Good Queries (relevance 0.60-0.69)

Pain Point Discovery:

"I hate my current CRM it is so frustrating"
→ Relevance: 0.60-0.64 | Found: Specific CRM complaints, feature wishlists

Tool Evaluation:

"AI tools that actually save time not just hype"
→ Relevance: 0.64-0.65 | Found: Real productivity gains, tool recommendations

❌ Weak Queries (avoid these patterns)

Too Abstract: "business opportunity growth potential" → 0.52-0.58 Non-English: "学习编程最好的方法" → 0.45-0.51

Query Formula Cheat Sheet

GoalPatternRelevance
Compare products"[A] vs [B] which should I use"0.70-0.81
Find switchers"why I switched from [A] to [B]"0.70-0.73
Money/hustle topics"[topic] that actually [works/makes money] not [scam/hype]"0.70-0.77
App recommendations"[category] apps which one is [accurate/best] and why"0.67-0.72
Pain points"I hate my current [tool] it is so [frustrating/slow]"0.60-0.64
Solutions seeking"[problem] tried everything what actually works"0.60-0.63

Example Workflows

Market Research:

curl -X POST https://reddapi.dev/api/v1/search/semantic \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "frustrated with project management tools switching from Jira", "limit": 50}'

Brand Monitoring (fast):

curl -X POST https://reddapi.dev/api/v1/search/vector \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "your-brand-name reviews complaints", "limit": 30}'

Deep Research (combine both):

  1. Vector search first to quickly scope the landscape
  2. Semantic search for deep analysis on specific angles

Time-Filtered Sentiment Tracking:

curl -X POST https://reddapi.dev/api/v1/search/vector \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "ChatGPT quality", "start_date": "2026-01-01", "end_date": "2026-03-18"}'

Tips

  1. Natural language works best - Ask questions like a human would
  2. Include context - "for small business" or "as a developer" improves results
  3. Combine emotion words - "frustrated", "love", "hate", "wish" find stronger opinions
  4. Filter by engagement - High upvotes/comments = validated pain points
  5. Use vector search for speed - ~5s vs ~20s for semantic
  6. Use date filters - Track sentiment changes over time periods

Error Handling

All endpoints return consistent error responses:

{
  "success": false,
  "error": "Error description",
  "message": {
    "title": "Human-readable title",
    "message": "Detailed explanation",
    "cta": "Suggested action",
    "ctaLink": "/pricing"
  }
}

Common status codes: 400 (invalid params), 401 (bad API key), 403 (plan limit), 429 (rate limit), 500 (server error)

平台分布

OpenClaw

73.96%
按下载量换算50,762

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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