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Market Analyst

natea/fitfinder

  • 49 installs
  • 4 repo stars
  • Updated October 26, 2025
  • natea/fitfinder

Helps with ai & agent building tasks.

About

market-analyst is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • market-analyst
  • AI & Agent Building
  • AI-coding skill

Market Analyst by the numbers

  • 49 all-time installs (skills.sh)
  • Ranked #7,391 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/natea/fitfinder --skill market-analyst

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Listed on Skillselion
Installs49
repo stars★ 4
Last updatedOctober 26, 2025
Repositorynatea/fitfinder ↗

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Market Analyst Skill

Purpose

This skill consumes outputs from the reddit-sentiment-analysis skill to perform meta-analysis across multiple products/games. It identifies:

  • Common patterns across successful products (what universally drives satisfaction)
  • Market gaps where demand exists but supply is lacking
  • Underserved segments with unmet needs
  • Novelty opportunities where unique approaches could succeed
  • Predicted hits based on cross-product sentiment intelligence
  • Strategic recommendations for product development and positioning

When to Use This Skill

Use this skill when you have:

  • ✅ Multiple sentiment analysis reports (2+ products/games analyzed)
  • ✅ Need to identify market opportunities across a product category
  • ✅ Want to predict which upcoming products will succeed
  • ✅ Looking for gaps in the market based on user sentiment
  • ✅ Need strategic recommendations for product development
  • ✅ Want to understand what makes products succeed or fail

Prerequisites

1. Input Data: 2+ Reddit sentiment analysis reports in /docs/

  • Generated by reddit-sentiment-analysis skill
  • Must follow standard format with LIKES/DISLIKES/WISHES sections
  • Recent data (ideally within same time period)

2. Analysis Scope: Clear product category (e.g., FPS games, productivity apps, streaming services)

Core Workflow

Phase 1: Data Ingestion and Normalization

1. Identify Available Sentiment Reports

  • Scan /docs/ for reddit-sentiment-*.md files
  • Parse each report to extract structured data
  • Validate format and completeness

2. Extract Key Data Points

For each product/game analyzed, extract:

{
  product_name: string,
  overall_sentiment: {positive: %, negative: %, neutral: %},
  likes: [
    {aspect: string, mentions: number, sentiment: %, quotes: []}
  ],
  dislikes: [
    {aspect: string, mentions: number, severity: string, quotes: []}
  ],
  wishes: [
    {feature: string, mentions: number, urgency: string, quotes: []}
  ],
  key_insights: [],
  competitor_mentions: {}
}

Phase 2: Cross-Product Pattern Analysis

3. Identify Universal Success Factors

Analyze LIKES across all products to find patterns:

Pattern Detection Algorithm:

// Group similar aspects across products
const commonLikes = groupSimilarAspects(allProducts.likes);

// Calculate frequency and consistency
for (aspect in commonLikes) {
  const frequency = countProducts(aspect);
  const avgSentiment = calculateAverage(aspect.sentiment);
  const consistency = calculateVariance(aspect.sentiment);

  if (frequency >= 50% && avgSentiment >= 85% && consistency < 15%) {
    markAs("Universal Success Factor");
  }
}

Success Factor Categories:

  • Gameplay/Functionality: Core mechanics, features, usability
  • Value Proposition: Pricing, content volume, value-for-money
  • Polish/Quality: Performance, visuals, stability, UX
  • Community/Social: Multiplayer, social features, community engagement
  • Innovation: Novel mechanics, creative approaches, unique features

4. Identify Universal Pain Points

Analyze DISLIKES across all products:

// Find recurring complaints
const commonDislikes = groupSimilarIssues(allProducts.dislikes);

// Classify by universality
for (issue in commonDislikes) {
  const frequency = countProducts(issue);
  const avgSeverity = calculateSeverity(issue);

  if (frequency >= 60% && avgSeverity === "HIGH") {
    markAs("Industry-Wide Problem");
  }
}

Pain Point Categories:

  • Monetization Issues: Aggressive MTX, pay-to-win, expensive pricing
  • Technical Problems: Performance, bugs, server issues
  • Design Flaws: Poor UX, frustrating mechanics, balance issues
  • Content/Feature Gaps: Missing features, lack of variety
  • Business Model Issues: Live service problems, abandonment fears

5. Analyze Wish Patterns

Examine WISHES to identify unmet demand:

// Find common wishes across products
const universalWishes = groupSimilarWishes(allProducts.wishes);

// Calculate demand intensity
for (wish in universalWishes) {
  const demandScore = wish.frequency * wish.avgUrgency * wish.mentions;

  if (demandScore > THRESHOLD) {
    markAs("High-Demand Unmet Need");
  }
}

Phase 3: Gap Identification and Market Opportunity Analysis

6. Identify Market Gaps

Gap Detection Framework:

Type 1: Feature Gaps (Widely wished for, nobody delivers)

IF: Wish appears in 3+ products
AND: Urgency >= MEDIUM across all
AND: No product currently delivers it
THEN: Feature Gap Opportunity

Type 2: Segment Gaps (Underserved audience)

IF: Common complaint about product not serving a specific need
AND: No product specifically targets that need
THEN: Segment Gap Opportunity

Type 3: Price/Value Gaps (Wrong pricing tier)

IF: Multiple products criticized for pricing
AND: Wishes mention "more affordable option" or "premium option"
AND: No product fills that price point
THEN: Price Gap Opportunity

Type 4: Business Model Gaps (Better service model needed)

IF: Common complaints about monetization/lifecycle
AND: Alternative model wished for across products
THEN: Business Model Gap Opportunity

7. Calculate Gap Priority Score

gapPriorityScore = (
  demandIntensity * 0.35 +        // How many people want it
  competitiveGap * 0.25 +          // How few products offer it
  urgencyLevel * 0.20 +            // How badly it's needed
  marketSize * 0.15 +              // Addressable market size
  feasibility * 0.05               // Technical/business feasibility
) * 100

Priority Tiers:

  • CRITICAL (90-100): Massive demand, no competition, urgent need
  • HIGH (75-89): Strong demand, minimal competition, clear need
  • MEDIUM (60-74): Moderate demand, some competition, growing need
  • LOW (40-59): Niche demand, crowded market, optional feature

Phase 4: Novelty Detection and Innovation Analysis

8. Identify Outlier Successes

Find products/features praised uniquely:

// Detect novelty
for (product in allProducts) {
  for (like in product.likes) {
    const uniqueness = calculateUniqueness(like, otherProducts);
    const sentiment = like.sentiment;

    if (uniqueness > 80% && sentiment > 85%) {
      markAs("Novelty Success", {
        feature: like.aspect,
        product: product.name,
        why_unique: analyzeWhy(like),
        replicability: assessReplicability(like)
      });
    }
  }
}

Novelty Categories:

  • Mechanic Innovation: Unique gameplay/feature never seen before
  • Design Innovation: Novel UX/UI approach or artistic direction
  • Business Model Innovation: New monetization or service model
  • Community Innovation: Unique social/multiplayer approach
  • Accessibility Innovation: Solving problems in new ways

9. Assess Novelty Replicability

For each novelty success:

  • Transferable to other products? (YES/NO/PARTIAL)
  • Category-specific or universal? (UNIVERSAL/CATEGORY/PRODUCT)
  • Competitive moat strength? (WEAK/MEDIUM/STRONG)
  • First-mover advantage duration? (MONTHS/YEARS/PERMANENT)

Phase 5: Predictive Analysis and Recommendations

10. Predict Likely Hits

Hit Prediction Algorithm:

function predictHitPotential(productConcept) {
  const score = {
    alignsWithSuccessFactors: 0,    // Does it have universal likes?
    avoidsCommonPitfalls: 0,         // Does it avoid universal dislikes?
    addressesUnmetNeeds: 0,          // Does it fill market gaps?
    hasNoveltyFactor: 0,             // Does it innovate?
    priceValueProposition: 0         // Is pricing right?
  };

  // Score each dimension (0-100)
  score.alignsWithSuccessFactors = checkAlignment(productConcept, universalSuccessFactors);
  score.avoidsCommonPitfalls = checkAvoidance(productConcept, universalPainPoints);
  score.addressesUnmetNeeds = checkGapFilling(productConcept, marketGaps);
  score.hasNoveltyFactor = checkNovelty(productConcept, noveltySuccesses);
  score.priceValueProposition = checkPricing(productConcept, pricingAnalysis);

  const hitProbability = (
    score.alignsWithSuccessFactors * 0.30 +
    score.avoidsCommonPitfalls * 0.25 +
    score.addressesUnmetNeeds * 0.25 +
    score.hasNoveltyFactor * 0.15 +
    score.priceValueProposition * 0.05
  );

  return {
    probability: hitProbability,
    confidence: calculateConfidence(dataQuality, sampleSize),
    breakdown: score,
    recommendations: generateRecommendations(score)
  };
}

11. Generate Strategic Recommendations

Product Development Recommendations:

### Must-Have Features (Universal Success Factors)
1. [Feature] - Present in X/Y products with Z% positive sentiment
   - Why it matters: [explanation]
   - How to implement: [guidance]

### Critical Pitfalls to Avoid (Universal Pain Points)
1. [Issue] - Complained about in X/Y products with Z severity
   - Why it fails: [explanation]
   - How to avoid: [guidance]

### Market Gap Opportunities (High Priority)
1. [Gap] - Priority Score: XX/100
   - Demand evidence: [data]
   - Competition: [current state]
   - Recommended approach: [strategy]

12. Create Market Opportunity Matrix

                    HIGH NOVELTY
                         |
    LOW DEMAND    Q2: Risky Innovation    Q1: Blue Ocean    HIGH DEMAND
                         |                       |
                  Q3: Avoid/Niche        Q4: Proven Demand
                         |
                    LOW NOVELTY

Q1 (High Demand + High Novelty): PRIORITY - Innovate in underserved areas
Q2 (Low Demand + High Novelty): RISKY - Innovation without market validation
Q3 (Low Demand + Low Novelty): AVOID - Crowded, low-interest space
Q4 (High Demand + Low Novelty): SAFE - Proven market, execution differentiator

Output Format

Market Analysis Report Structure

# Market Analysis Report: [Product Category]

**Analysis Date**: [Date]
**Products Analyzed**: [List]
**Sentiment Reports Used**: [Number]
**Total Data Points**: [Posts + Comments analyzed]

---

## Executive Summary

[2-3 paragraph overview of key findings, top opportunities, major risks]

---

## Section 1: Universal Success Factors

### What Drives Success Across All Products

1. **[Success Factor Name]** (appears in X/Y products, Z% avg positive sentiment)
   - **Evidence**: [Quotes from multiple products]
   - **Why it works**: [Psychological/practical explanation]
   - **Implementation guidance**: [How to deliver this]
   - **Products excelling**: [Examples]

[Repeat for 5-7 success factors]

### Success Factor Summary Table

| Factor | Frequency | Avg Sentiment | Consistency | Priority |
|--------|-----------|---------------|-------------|----------|
| [Factor 1] | 5/5 products | 92% | High | CRITICAL |
| [Factor 2] | 4/5 products | 87% | Medium | HIGH |
...

---

## Section 2: Universal Pain Points

### What Consistently Fails Across Products

1. **[Pain Point Name]** (appears in X/Y products, Z severity)
   - **Evidence**: [Quotes showing frustration]
   - **Why it fails**: [Root cause analysis]
   - **How to avoid**: [Prevention strategy]
   - **Products struggling**: [Examples]

[Repeat for 5-7 pain points]

### Pain Point Summary Table

| Issue | Frequency | Avg Severity | Impact | Avoidability |
|-------|-----------|--------------|--------|--------------|
| [Issue 1] | 5/5 products | CRITICAL | High | Easy |
| [Issue 2] | 4/5 products | HIGH | Medium | Hard |
...

---

## Section 3: Market Gaps & Opportunities

### High-Priority Gaps (Score 75-100)

1. **[Gap Name]** - Priority Score: XX/100
   - **Type**: [Feature/Segment/Price/Business Model]
   - **Demand Evidence**:
     - Mentioned in X/Y products
     - Y total mentions, Z% urgency HIGH
     - Representative quotes: "[quote 1]", "[quote 2]"
   - **Current Competition**: [Who's attempting this, if anyone]
   - **Market Size Estimate**: [TAM/SAM if calculable]
   - **Recommended Approach**: [Strategy to fill gap]
   - **Risks**: [Challenges to address]
   - **Timeline to Market**: [Estimate]

[Repeat for all high-priority gaps]

### Medium-Priority Gaps (Score 60-74)
[Similar structure, condensed]

### Gap Opportunity Matrix

Demand Intensity vs. Competitive Gap [Visual representation of opportunities]


---

## Section 4: Novelty & Innovation Analysis

### Successful Innovations (Outlier Wins)

1. **[Innovation Name]** from [Product]
   - **What makes it unique**: [Description]
   - **Sentiment**: [% positive, mentions]
   - **Evidence**: [Quotes praising novelty]
   - **Replicability**: [EASY/MEDIUM/HARD]
   - **Transferability**: [Which categories could use this]
   - **Competitive moat**: [WEAK/MEDIUM/STRONG]
   - **Recommendation**: [Should others copy? How?]

[Repeat for 3-5 novelty successes]

### Innovation Categories

- **Mechanic Innovations**: [List]
- **Design Innovations**: [List]
- **Business Model Innovations**: [List]
- **Community Innovations**: [List]

---

## Section 5: Predicted Hits & Strategic Recommendations

### Upcoming Products/Concepts Likely to Succeed

1. **[Product/Concept]** - Hit Probability: XX%
   - **Why it will succeed**:
     - ✅ Aligns with success factors: [Score/100]
     - ✅ Avoids common pitfalls: [Score/100]
     - ✅ Addresses unmet needs: [Score/100]
     - ✅ Has novelty factor: [Score/100]
     - ✅ Price/value proposition: [Score/100]
   - **Key strengths**: [List]
   - **Potential risks**: [List]
   - **Confidence level**: [HIGH/MEDIUM/LOW based on data]

[Repeat for 3-5 predicted hits]

### Product Development Blueprint

**If creating a new product in this category, it MUST:**

✅ **Include These (Universal Success Factors)**
1. [Factor 1] - Critical
2. [Factor 2] - High priority
3. [Factor 3] - Medium priority
...

❌ **Avoid These (Universal Pain Points)**
1. [Pitfall 1] - Critical to avoid
2. [Pitfall 2] - High priority to avoid
...

🎯 **Target These Gaps (Market Opportunities)**
1. [Gap 1] - Priority Score: XX
2. [Gap 2] - Priority Score: XX
...

💡 **Consider These Innovations (Novelty Opportunities)**
1. [Innovation 1] - Transferable from [Product]
2. [Innovation 2] - Novel approach to [Problem]
...

### Strategic Positioning Recommendations

**Blue Ocean Opportunities** (High demand + High novelty):
- [Opportunity 1]: [Description and strategy]
- [Opportunity 2]: [Description and strategy]

**Safe Bets** (High demand + Proven approach):
- [Opportunity 1]: [Description and execution focus]

**Risky Innovations** (Low current demand + High novelty):
- [Opportunity 1]: [Why risky, when it might pay off]

**Avoid Zones** (Low demand + Low novelty):
- [Space 1]: [Why to avoid]

---

## Section 6: Trend Analysis

### Emerging Trends

1. **[Trend Name]**
   - **Evidence**: [Sentiment shifts, wish patterns]
   - **Trajectory**: [Growing/Stable/Declining]
   - **Opportunity window**: [Timeframe]
   - **First-mover advantage**: [Strength]

### Dying Trends

1. **[Trend Name]**
   - **Evidence**: [Negative sentiment increase]
   - **Why it's failing**: [Analysis]
   - **Avoid investing in**: [Specific approaches]

---

## Section 7: Competitive Intelligence

### Competitor Positioning

| Product | Strength | Weakness | Sentiment | Market Position |
|---------|----------|----------|-----------|-----------------|
| [Product 1] | [Core strength] | [Main weakness] | XX% positive | Leader/Challenger |
...

### Competitive Gaps

Products are NOT competing on:
- [Dimension 1]: Opportunity for differentiation
- [Dimension 2]: Blue ocean potential

---

## Appendices

### A. Data Quality & Methodology

- **Products analyzed**: [List with report dates]
- **Total posts/comments**: [Numbers]
- **Confidence scores**: [How calculated]
- **Limitations**: [Data gaps, biases, timeframe]

### B. Detailed Calculations

[Show priority score calculations, hit prediction formulas]

### C. Raw Data Summary

[Tables of all extracted data points]

---

## Actionable Next Steps

1. **Immediate (This week)**:
   - [Action based on critical findings]

2. **Short-term (This month)**:
   - [Actions based on high-priority gaps]

3. **Long-term (This quarter)**:
   - [Strategic positioning moves]

---

**Report Generated By**: Market Analyst Skill v1.0
**Based On**: [X] Reddit Sentiment Analysis Reports
**Data Sources**: Reddit (r/[subreddits])
**Analysis Date**: [Date]

Implementation Protocol

Step 1: Create Analysis Plan

TodoWrite([
  "Identify and load all sentiment analysis reports",
  "Extract structured data from each report",
  "Identify universal success factors across products",
  "Identify universal pain points across products",
  "Analyze wish patterns for unmet demand",
  "Calculate market gap priority scores",
  "Detect novelty successes and assess replicability",
  "Predict likely hits and generate recommendations",
  "Create market opportunity matrix",
  "Generate comprehensive market analysis report"
])

Step 2: Data Loading

CRITICAL: Batch all file reads in parallel:

[Single Message - Parallel Report Loading]:
  Read("/docs/reddit-sentiment-analysis-game1.md")
  Read("/docs/reddit-sentiment-analysis-game2.md")
  Read("/docs/reddit-sentiment-analysis-game3.md")
  Read("/docs/reddit-sentiment-analysis-game4.md")
  Read("/docs/reddit-sentiment-analysis-game5.md")

Step 3: Cross-Product Analysis

Process all reports simultaneously to identify:

  • Common likes (appear in 50%+ of products)
  • Common dislikes (appear in 60%+ of products)
  • Common wishes (appear in 40%+ of products)
  • Unique features (appear in <25% of products but highly praised)

Step 4: Gap Analysis

For each identified wish pattern: 1. Calculate demand score (frequency × urgency × mentions) 2. Assess competitive landscape (who's trying to fill this?) 3. Estimate market size (based on product reach) 4. Assign priority score

Step 5: Report Generation

Save comprehensive report to: /docs/market-analysis-[category]-[date].md

Best Practices

DO:

✅ Analyze minimum 3 products for meaningful patterns ✅ Use recent sentiment data (within 3 months) ✅ Consider product category context (FPS games ≠ puzzle games) ✅ Weight by sample size (1000 comments > 50 comments) ✅ Look for sentiment intensity, not just direction ✅ Consider temporal trends (sentiment changing over time) ✅ Cross-reference competitor mentions ✅ Validate gaps with market research

DON'T:

❌ Mix incompatible product categories (games + productivity apps) ❌ Over-generalize from small sample sizes ❌ Ignore context (niche vs. mainstream products) ❌ Assume correlation = causation ❌ Miss seasonal/event-driven sentiment spikes ❌ Ignore demographic differences in sentiment ❌ Recommend unfeasible solutions

Integration with Other Skills

This skill works perfectly with:

  • reddit-sentiment-analysis: Primary data source
  • stream-chain: Pipeline sentiment → market analysis
  • competitive-analysis: Deep dive on specific competitors
  • product-roadmap: Prioritize features based on gaps
  • trend-analysis: Track sentiment evolution over time

Example Usage Scenarios

Scenario 1: Gaming Market Analysis

Input: 5 FPS game sentiment reports
Output:
- Success factors: Gunplay feel, map variety, progression
- Pain points: Aggressive monetization, yearly release cycles
- Gaps: Affordable tactical shooter, 2-3 year lifecycles
- Predicted hit: Tactical shooter at $20-30 with 3-year support

Scenario 2: SaaS Product Analysis

Input: 4 productivity tool sentiment reports
Output:
- Success factors: Clean UX, integration ecosystem, offline mode
- Pain points: Confusing pricing, feature bloat, poor onboarding
- Gaps: Simple, focused tool for [specific use case]
- Predicted hit: Specialized tool doing one thing excellently

Scenario 3: Streaming Service Analysis

Input: 3 streaming platform sentiment reports
Output:
- Success factors: Content library, UI/UX, affordable pricing
- Pain points: Content removal, ads in paid tiers, app crashes
- Gaps: Ad-free budget tier, permanent content library
- Predicted hit: Niche streaming service with ownership model

Summary

The Market Analyst Skill transforms individual sentiment analyses into strategic market intelligence by:

1. Finding universal patterns across products (what always works, what always fails) 2. Identifying market gaps where demand exists but supply doesn't 3. Detecting novelty successes that could be replicated or adapted 4. Predicting likely hits based on alignment with success patterns 5. Generating strategic recommendations for product development and positioning

This enables data-driven decision-making for:

  • Product managers prioritizing features
  • Entrepreneurs identifying market opportunities
  • Investors evaluating product-market fit
  • Designers understanding user needs
  • Strategists positioning against competitors

The output is a comprehensive, evidence-based market analysis report ready for strategic planning and product development decisions.

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