reddit-product-viability
Scrape and analyze Reddit for real user signals about product viability, pain severity, willingness to pay, and competitor saturation. Validate product ideas before building by systematically analyzing discussions, complaints, feature requests, and purchasing behavior across relevant subreddits. Integrates with Firecrawl for scraping, Supabase for storage, and Superset for trend visualization.
Profile →npx skills add majiayu000/claude-skill-registry --skill reddit-product-viability --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Crates a framework to validate product ideas by scraping Reddit for signals related to real demand, pain severity, willingness to pay, and competitor saturation. It uses Reddit API with Firecrawl for scraping, stores data in Supabase, analyzes with NLP tools, and visualizes via Superset dashboards. It also tracks findings in Notion and supports scheduled monitoring.
How it works
- Uses a Product Viability Validation Framework to assess four dimensions: Real Demand Signals, Pain Severity, Willingness to Pay, and Competitor Saturation.
- Performs Reddit scraping across specified subreddits with keywords, gathering post data and top comments within a defined timeframe.
- Analyzes demand signals by computing metrics like total posts, average score, average comments, and top posts.
- Assesses pain severity by scanning posts for predefined pain indicators and scoring occurrences.
- Examines pricing discussions by detecting price-related keywords and collecting relevant posts.
- Attempts a competitor saturation analysis by inspecting mentions and sentiment related to competitors in comments (requires NLP sentiment analysis).
- Stores raw posts and a viability analysis summary in Supabase tables reddit_posts and viability_analysis.
- Generates a human-readable viability report summarizing demand, pain, pricing, and recommendations.
When to use it
- When you need to validate product ideas before investing development time
- When assessing market demand through real user conversations
- When identifying pain points and severity across target segments
- When evaluating willingness to pay based on user discussions
- When analyzing competitor saturation and gaps in solutions
- When discovering feature requests and unmet needs
- When monitoring product-market fit signals over time
- When researching SaaS alternative opportunities
What it can touch
- Reddit API via praw
- Firecrawl for scraping integration
- Supabase for data storage and upsert/insert operations
- NLP tools (sentiment analysis) for analysis of comments
- Superset for dashboards and trend visualization
- Notion for tracking findings (optional integration)
Caveats
- Requires Reddit API key (free tier: 100 requests/minute) and Firecrawl API key, plus a Supabase project with pgvector and a Superset instance
- The Competitor Saturation analysis is labeled as simplified and may require additional customization or NLP/NER to accurately extract competitor mentions
- The skill stores and analyzes data with placeholders for certain fields (e.g., product_idea) and may need schema alignment in your deployment
- Supabase schema assumes specific tables and columns; ensure the environment matches or adapt accordingly
- Some sections rely on optional integrations (OpenAI, Perplexity, Notion); enable as needed
# Reddit Product Viability Research ## When to Use This Skill Use this skill when you need to: - **Validate product ideas** before investing development time - **Assess market demand** through real user conversations - **Identify pain points** and severity across target segments - **Evaluate willingness to pay** based on user discussions - **Analyze competitor saturation** and gaps in solutions - **Discover feature requests** and unmet needs - **Monitor product-market fit signals** over time - **Research SaaS alternative opportunities** (like SAP Concur, Ariba alternatives) ## Core Capabilities ### Product Viability Validation Framework Systematically evaluate four critical dimensions: 1. **Real Demand Signals** - Volume of discussions about the problem - Frequency of complaints and pain points - Emotional intensity in user posts - Problem persistence over time 2. **Pain Severity Assessment** - Impact on users' work/life - Workarounds currently being used - Time/money currently wasted - Urgency of need for solution 3. **Willingness to Pay** - Current spending on alternatives - Budget discussions and constraints - "Shut up and take my money" signals - Pricing tolerance indicators 4.
- When to Use This Skill
- Core Capabilities
- Product Viability Validation Framework
- Technical Implementation
- Prerequisites
- Required Access
- Optional Integrations
- Python Dependencies
- Implementation Patterns
- Product Validation Prompt Template
- Reddit Scraping Script
- Supabase Schema
- Superset Dashboard SQL
- Integration Points
What does the reddit-product-viability skill do?
Scrape and analyze Reddit for real user signals about product viability, pain severity, willingness to pay, and competitor saturation. Validate product ideas before building by systematically analyzing discussions, complaints, feature requests, and purchasing behavior across relevant subreddits. Integrates with Firecrawl for scraping, Supabase for storage, and Superset for trend visualization.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill reddit-product-viability --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.