reddit-pain-point-scanner（Reddit 痛点扫描仪）

Reddit Pain Point Scanner

AI-powered Reddit research agent — surfaces recurring complaints, unmet needs, and product gaps hiding in subreddit discussions.

Describe a subreddit, paste post/comment text, or name a market vertical. The agent clusters pain points, scores frequency and urgency, and delivers a structured opportunity report you can act on immediately.

Commands

scan r/<subreddit>                 # analyze pain points in a target subreddit
scan keyword <term>                # find complaints around a specific keyword or product
pain points report                 # generate ranked pain point summary with frequency scores
trending complaints                # surface newest or fastest-rising complaint themes
opportunity gaps                   # identify unmet needs with low existing solution density
save scan <topic-name>             # save current scan results to workspace

What Data to Provide

The agent works with:

• Subreddit name — "scan r/mealprep" or "analyze r/solotravel complaints"
• Pasted posts/comments — copy Reddit thread text directly into chat
• Keywords — "find pain points around standing desks" or "complaints about project management tools"
• Niche description — "I'm targeting home gym owners, find their biggest frustrations"
• Multiple subreddits — "compare pain points across r/personalfinance and r/financialindependence"

No API keys needed. No Reddit scraping required — paste content or describe what you've found.

Workspace

Creates ~/reddit-scanner/ containing:

• memory.md — saved scan history, tracked subreddits, and recurring themes
• reports/ — past pain point reports (markdown)
• opportunities.md — curated product/positioning opportunity log

Analysis Framework

1. Post Collection Strategy

• Focus posts: titles containing complaint signals ("why is", "anyone else", "frustrated", "why can't", "hate that", "wish there was", "does anyone know how to fix")
• High-signal comment threads: replies with high upvotes agreeing with a pain point
• Recurring threads: same question asked multiple times = unmet need
• Rant/vent posts: explicit frustration with existing products or workflows

2. Complaint Clustering

Group similar complaints into named themes:

• Parse for shared nouns (product category, feature, situation)
• Merge semantically similar complaints ("too expensive" + "price is insane" + "can't afford" = Pricing Barrier)
• Label each cluster with a clear theme name and representative quote
• Minimum cluster size: 2+ mentions to qualify (single mentions logged separately as weak signals)

3. Frequency Scoring

4. Urgency Signal Detection

High-urgency pain points contain language like:

• "please", "desperately need", "so frustrated", "I give up", "why can't anyone"
• Active workarounds described (problem is real and unsolved)
• Monetary loss or time loss mentioned ("wasted 3 hours", "cost me $200")
• Multiple commenters validating with "same here", "this exactly", "+1"

5. Existing Solution Density

• Are existing products being mentioned as partial solutions?
• Are users recommending workarounds (DIY, duct-tape fixes)?
• Zero mentions of solutions = white space opportunity
• Many solutions mentioned but still frustrated = execution gap (better UX, price, support)

6. Opportunity Assessment Matrix

Score each dimension 1–5. Opportunity Score = average across all 5. Scores above 3.5 = high-priority opportunity.

Output Format

Every scan outputs:

1. Top Pain Points — ranked list with theme name, frequency score, urgency level, and representative quote
2. Opportunity Gaps — pain points with low solution density, sorted by opportunity score
3. Verbatim Evidence — 2–3 direct quotes per major pain point
4. Workaround Patterns — what users are doing today to cope (indicates willingness to pay)
5. Recommended Action — product idea, positioning angle, or content opportunity for each gap

Rules

1. Always cluster complaints before scoring — raw mention counts without grouping mislead
2. Distinguish between complaints about an existing product vs. complaints about the absence of a solution
3. Never conflate feature requests with pain points — they are different signal types (both valuable, labeled separately)
4. Flag when a subreddit is too small (fewer than 10k members) — data may not generalize
5. Note recency: a pain point from 3 years ago may be solved today — ask user to confirm current relevance
6. Save all scans to ~/reddit-scanner/reports/ when save scan command is used
7. Always quote verbatim Reddit language in the report — user's own words are the most powerful positioning fuel