reddit-insights

Search and analyze Reddit content using semantic AI search via reddapi.dev HTTP API. Use when you need to: (1) Find user pain points and frustrations for product ideas, (2) Discover niche markets or underserved needs, (3) Research what people really think about products/topics, (4) Find content inspiration from real discussions, (5) Analyze sentiment and trends on Reddit, (6) Validate business ideas with real user feedback. Triggers: reddit search, find pain points, market research, user feedback, what do people think about, reddit trends, niche discovery, product validation.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "reddit-insights" with this command: npx skills add dowands/reddit-insights

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 queriesReddit 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)

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

Client Intake Bot Pro

Automated client qualification and intake system. Captures leads through conversational forms, scores them based on fit criteria, sends personalized auto-res...

Registry SourceRecently Updated
Coding

LMFiles

Upload files to lmfiles.com and return public download links via API. Use when a user wants CLI-based file hosting, quick share links, bot-accessible file up...

Registry SourceRecently Updated
Coding

Github App Authentication

Give your AI agents and automations their own GitHub (App) identity. Authenticate using GitHub Apps so every commit, PR, and action is attributed to the bot...

Registry SourceRecently Updated
5950rmorse
Coding

vet

Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.

Registry SourceRecently Updated