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name: startup-review-mining description: "Systematic extraction of pain points, feature gaps, switching triggers, and opportunities from review sources (B2B review sites, app stores, forums, communities, issue trackers). Includes bias hygiene, taxonomy building, and turning insights into experiments."

Review Mining Skill — Quick Reference

This skill extracts recurring customer pain and constraints from reviews and testimonials, then converts them into product bets and experiments. Treat reviews as a biased sample; triangulate before betting.

Key Distinction from software-ux-research:

• software-ux-research = UI/UX pain points only
• startup-review-mining (this skill) = ALL pain dimensions (pricing, support, integration, performance, onboarding, value gaps)

Modern Best Practices (Dec 2025):

• Start with source hygiene: sampling plan, platform skews, and fake-review defenses.
• Build a taxonomy (theme × segment × severity) before counting keywords.
• Convert insights into bets with explicit success metrics and decision rules.
• Handle customer/market data with purpose limitation, retention, and access controls.

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When to Use This Skill

Invoke when users ask for:

• Pain point extraction from reviews (any source)
• Competitive weakness analysis
• Feature gap identification
• Switching trigger analysis (why customers leave competitors)
• Market opportunity discovery through customer complaints
• Review sentiment analysis across platforms
• B2B software evaluation (G2, Capterra, TrustRadius)
• B2C app analysis (App Store, Play Store)
• Community sentiment (Reddit, HN, Twitter/X)
• Support pain patterns (forums, tickets, Stack Overflow)

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Quick Reference Table

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Review Source Hygiene (Dec 2025)

Sampling and Bias Checklist

• Define the time window (e.g., last 6–12 months) and why.
• Sample across ratings (not only 1-star or 5-star).
• Separate segments (SMB vs enterprise; dev vs non-dev; geo; regulated vs not).
• Flag incentives and moderation (some platforms skew toward promoters).

Fake Review and Manipulation Defenses

• Look for repeated phrasing, unnatural timing bursts, and identical complaints/praise across accounts.
• Prefer "verified" signals when available; still spot-check.
• Treat suspicious clusters as weak evidence until corroborated elsewhere.

Advanced Detection (2025-2026):

Key detection signals:

• Timing bursts: 10+ reviews in 24 hours = suspicious
• Profile sparsity: Single-review accounts posting detailed praise
• Cross-platform patterns: Identical text across G2/Capterra/TrustRadius
• GenAI markers: Overly formal language, generic praise without specifics

GenAI Challenge (2025): ChatGPT-generated reviews are harder to detect because they mimic natural language patterns. Require multimodal detection (text + behavioral signals) for 90%+ accuracy.

Compliance note: if you use reviews for marketing claims or comparisons, be aware of the FTC's consumer reviews/testimonials rule (16 CFR Part 465). Source: https://www.ftc.gov/legal-library/browse/federal-register-notices/16-cfr-part-465-trade-regulation-rule-use-consumer-reviews-testimonials-final-rule

The 7 Pain Dimensions

Reviews reveal pain across 7 dimensions - not just UI/UX:

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Decision Tree: Choosing Mining Approach

CopyReview Mining Need: [What do you want to learn?]
    |
    +-- Finding pain points in a market?
    |   +-- B2B software? → G2 + Capterra + TrustRadius (references/source-by-source-extraction.md#b2b)
    |   +-- Consumer app? → App Store + Play Store (references/source-by-source-extraction.md#b2c)
    |   +-- Developer tool? → GitHub Issues + Stack Overflow + HN (references/source-by-source-extraction.md#tech)
    |
    +-- Analyzing specific competitors?
    |   +-- Direct comparison? → Competitor Weakness Matrix (assets/competitor-weakness-matrix.md)
    |   +-- Why customers switch? → Switching Trigger Analysis (assets/switching-trigger-analysis.md)
    |   +-- Feature gaps? → Feature Request Aggregator (assets/feature-request-aggregator.md)
    |
    +-- Finding opportunities?
    |   +-- Quick wins (<2 weeks)? → Opportunity Template (assets/opportunity-from-reviews.md#quick-wins)
    |   +-- Medium bets (2-8 weeks)? → Opportunity Template (assets/opportunity-from-reviews.md#medium)
    |   +-- Big differentiation? → Opportunity Template (assets/opportunity-from-reviews.md#big-bets)
    |
    +-- Understanding sentiment?
    |   +-- Real-time complaints? → Twitter/X monitoring (references/source-by-source-extraction.md#social)
    |   +-- Community opinion? → Reddit + HN (references/source-by-source-extraction.md#community)
    |   +-- Launch feedback? → ProductHunt (references/source-by-source-extraction.md#producthunt)
    |
    +-- Comprehensive analysis?
        +-- Full market research? → Review Mining Report (assets/review-mining-report.md)

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Review Sources (Complete Guide)

Tier 1: Very High Signal (Start Here)

Tier 2: High Signal

Tier 3: Medium Signal

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Navigation: Resources

Extraction Methodology

• references/source-by-source-extraction.md - Platform-specific extraction guides for each source with search queries, filters, and export methods

Analysis Frameworks

• references/pain-categorization-framework.md - 7-dimension pain taxonomy with severity scoring and prioritization
• references/sentiment-analysis-patterns.md - Identifying pain vs praise, detecting intensity, and spotting trends

Competitive Intelligence

• references/competitor-review-comparison.md - Cross-competitor analysis methodology with switching trigger identification

Opportunity Mapping

• references/review-to-opportunity-mapping.md - Converting pain points to actionable product opportunities with ROI estimation

External References

• data/sources.json - Review platform URLs, API endpoints, and tool recommendations

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Navigation: Templates

Full Reports

• assets/review-mining-report.md - Comprehensive mining report with all dimensions

Source-Specific Extraction

• assets/b2b-review-extraction.md - B2B software review extraction (G2, Capterra, TrustRadius)
• assets/b2c-review-extraction.md - Consumer app review extraction (App Store, Play Store)
• assets/community-sentiment.md - Community sentiment analysis (Reddit, HN, ProductHunt)

Competitive Analysis

• assets/competitor-weakness-matrix.md - Cross-competitor weakness comparison
• assets/switching-trigger-analysis.md - Why customers leave competitors
• assets/feature-request-aggregator.md - Unmet needs collection

Pain Analysis

• assets/pain-point-extraction-worksheet.md - Structured worksheet for 7-dimension pain extraction

Opportunity Output

• assets/opportunity-from-reviews.md - Pain → Opportunity conversion

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Trend Awareness Protocol

IMPORTANT: When users ask about customer pain points or reviews, you MUST use WebSearch to check current information before answering.

Trigger Conditions

• "What are users saying about [product/competitor]?"
• "What are the pain points in [market]?"
• "Recent reviews for [product]?"
• "What are customers complaining about?"
• "What features are users requesting?"
• "Sentiment analysis for [product/market]?"

Required Searches

1. Search: "[product] reviews 2026"
2. Search: "[product] complaints Reddit 2026"
3. Search: "[market] user pain points 2026"
4. Search: "[competitor] G2 reviews"

What to Report

After searching, provide:

• Current sentiment: What users are saying NOW
• Trending complaints: Most common recent issues
• Feature requests: What users are asking for
• Competitive gaps: Problems competitors aren't solving

Example Topics (verify with fresh search)

• G2, Capterra, TrustRadius reviews
• Reddit discussions and complaints
• App Store and Play Store reviews
• Twitter/X sentiment
• Product Hunt feedback
• HackerNews discussions

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Related Skills

• ../software-ux-research/SKILL.md - UI/UX Sibling: UI/UX-specific research (this skill goes broader)
• ../startup-idea-validation/SKILL.md - Consumer: Uses review mining data for validation scoring
• ../startup-trend-prediction/SKILL.md - Parallel: Combines with trend data for timing
• ../router-startup/SKILL.md - Orchestrator: Routes to this skill for pain discovery
• ../product-management/SKILL.md - Consumer: Uses pain points for discovery and roadmapping

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Operational Workflow

Standard Mining Flow

Copy1. SCOPE (Define Target)
   +-- Which product/market to analyze?
   +-- Which competitors to include?
   +-- Time range (last 6-12 months recommended)

2. EXTRACT (Gather Data)
   +-- B2B: G2 → Capterra → TrustRadius
   +-- B2C: App Store → Play Store
   +-- Tech: GitHub → HN → Stack Overflow
   +-- Social: Twitter/X → Reddit → LinkedIn

3. CATEGORIZE (7 Dimensions)
   +-- UI/UX Pain
   +-- Pricing Pain
   +-- Support Pain
   +-- Integration Pain
   +-- Performance Pain
   +-- Onboarding Pain
   +-- Value Pain

4. SCORE (Prioritize)
   +-- Frequency (how often mentioned)
   +-- Severity (how painful)
   +-- Addressability (can we solve it?)

5. MAP (Convert to Opportunities)
   +-- Quick Wins (<2 weeks)
   +-- Medium Bets (2-8 weeks)
   +-- Big Differentiation (8+ weeks)

6. OUTPUT (Deliverable)
   +-- Review Mining Report
   +-- Competitor Weakness Matrix
   +-- Opportunity Backlog

Integration with Validation Pipeline

CopyUSER ASKS                              SKILL FLOW
──────────────────────────────────────────────────────────────
"Find opportunities in X market"  → startup-review-mining → Pain Report
                                         ↓
"What's trending?"               → startup-trend-prediction → Timing Analysis
                                         ↓
"Should we build this?"          → startup-idea-validation → GO/NO-GO Score
                                         ↓
"What skills do we need?"        → router-startup → Implementation Path

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Turning Insights Into Bets

• Convert pains → opportunities using assets/opportunity-from-reviews.md.
• Turn opportunities into decisions using:
  • ../product-management/assets/strategy/opportunity-assessment.md
  • ../startup-idea-validation/assets/validation-experiment-planner.md

Do / Avoid (Dec 2025)

Do

• Keep an audit trail (source links, sampling notes, timestamps).
• Score insights by frequency × severity × segment importance × addressability.
• Triangulate top insights via interviews, support tickets, or usage data.

Avoid

• Keyword counting without context or segmentation.
• Treating sentiment as demand without willingness-to-pay signals.
• Copying competitor feature requests without understanding the underlying job.

What Good Looks Like

• Coverage: a defined time window and segment tags (plan documented, not ad-hoc scraping).
• Taxonomy: 10–30 themes with frequency + severity, each backed by verbatim quotes and links.
• Quality: spot-check a sample of clustered/summarized outputs and log corrections.
• Actionability: top themes become hypotheses with experiments and decision thresholds.
• Compliance: respect platform terms and detect suspicious/fake-review patterns (audit trail preserved).

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

• Summarization and clustering: require spot-checks and preserve source links.
• Automated extraction: log prompts/settings and keep reproducibility notes.

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Usage Notes

For Claude: When user asks to "find pain points" or "analyze reviews":

1. Ask which market/product/competitors to analyze
2. Use source-by-source-extraction.md for platform-specific queries
3. Categorize findings into 7 pain dimensions
4. Score by frequency × severity × addressability
5. Output using review-mining-report.md template

For Claude: When user asks to "find opportunities":

1. First extract pain points (above workflow)
2. Use review-to-opportunity-mapping.md to convert
3. Categorize into Quick Wins / Medium Bets / Big Differentiation
4. Feed to startup-idea-validation for scoring

Output Formats:

• Pain points: 7-dimension matrix with severity scores
• Competitor analysis: Weakness comparison with quote evidence
• Opportunities: Prioritized backlog with effort/impact estimates
• Switching triggers: Why customers leave (ranked by frequency)

Key Principle: Reviews are the voice of the customer. Mine them systematically.