# Market Insight Product Selection

> Adaptive product-selection methodology built on multi-source signals and "voice of customer" (VoC): validate demand, momentum, competitive intensity, and user pain points from reviews/comments to produce a confident shortlist or a focused go/no-go. **When to use**: - User is asking about trends/popularity (Tiktok/Amazon/etc.), or what's selling well in a market (US/UK/etc.) - User asks for **single-category / single-product** trend analysis - User wants market analysis, market research, or competitive landscape insights - User is researching consumer preferences, demand patterns, or market dynamics - User needs help deciding what products to sell, source, or invest in - User is exploring product opportunities, winning products, or profitable niches

- Skill: `veritasian/market-insight-product-selection` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add veritasian/market-insight-product-selection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/veritasian/market-insight-product-selection/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: veritasian (https://skillmd.com/u/veritasian)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/veritasian/market-insight-product-selection

---


An adaptive, evidence-first workflow for turning messy "trend" signals into a decision-ready shortlist using multi-source triangulation + VoC.

# How to Use (Adaptive)

Use this skill to produce a **repeatable selection pipeline** from messy signals. If the user asks for "what to sell / trending products / hot selling products", follow this order:

1. **Trend Radar** → build a *trend-driven candidate list* (10–30) with evidence
2. **Top Picks** → rank and select Top3–8 with an explicit decision rule
3. **Deep Dive Packs** → deep analysis for each Top pick (as comprehensive as evidence allows)
4. **Decision & Action Guide** → Go / Cautious / No-Go + next steps

## Core Principle: Evidence Density (Why Your "Reasons" Become Better)

- **User intent overrides the skill**: if any guidance here conflicts with what the user explicitly asked for (platform/channel, market, timeframe, constraints, format), **follow the user request first**.  
  - Example: user asks for **TikTok hot products** → the primary candidate pool and examples should be **TikTok products** (TikTok Shop / TikTok trends / creator velocity).  
  - You may still use **other channels** (YouTube/Reddit/Amazon/etc.) for **supporting VoC / triangulation**, but don't replace the requested platform with "defaults".
- **Evidence first**: don't write deep "reasons" until you have enough concrete evidence.
- **Question-driven collection**: collect enough information based on the user's question (e.g., action cameras: specs/capabilities + pricing/positioning + competitors + VoC).
- **Concrete product/model deep dive requires a pack**: whenever analysis touches a specific product/model/listing (user-provided OR discovered during category analysis), collect a **Product Deep Dive Evidence Pack** (below).
- **If evidence is thin**: explicitly output **Data Gaps + Next Collection Step** instead of inventing confident reasons.

## Routing Shortcut

- **Category trend** → Demand & Scope → Radar → (optional) Deep Dive 3–8 products/models → Final
- **Specific candidates** → Deep Dive → Brain → (optional) Scorecard → Final

## Dependencies

- `matplotlib`/`seaborn` for chart generation

---

# Steps

## 1) Demand & Scope (Optional; recommended for broad category questions)

**Purpose**: Conduct deep, multi-source data analysis with cross-validation. Generate actionable selection recommendations with clear conclusion, analysis logic, and data evidence.

#### Data Collection

**Maximize data source calls** (≥3 sources):
- **Web search**: General market intelligence, news, and social phenomena
- **Sales platforms**: Amazon, Alibaba, Shopee, Shein (via `web_search` or specialized tools)
- **Trend data**: Google Trends, Amazon search trends
- **Social media**: YouTube, Reddit

**Deep dive with `web_fetch` tool**: When search results return promising URLs, use the `web_fetch` tool to extract detailed information from web pages (e.g., full article content, detailed product specs, in-depth reviews)

#### Deep Search Protocol

**Core flow**: Search → Extract entities → Search DEEPER → Check saturation → Continue until complete

⚠️ Deep search is **NOT limited to 2-3 rounds**. Continue until saturation signals detected.

##### Iterative Search Rounds

**Round1 - Broad (3-6 queries)**: Break topic into dimensions (market size, consumer preferences, competition, technology, community feedback).

**Round2 - Deep (2-4 queries)**: From R1 findings, drill into specific brands, technologies, pain points, suppliers.

**Round3+ - Until saturation**: New entities → explore | Unknowns → fill | Claims → verify | Contradictions → resolve

##### Extract & Map (Entity Extraction)

After each `web_fetch`, extract entities and relationships to identify next investigation points.

**Entity extraction prompt template**:
```
Extract from this content:
1. Key entities: products, brands, companies, technologies, suppliers
2. Relationships: [Product] uses [Component] supplied by [Vendor]
3. Next investigation: what should be explored based on these findings
```

**Example**: Found "Xiaomi Band 9 uses Goodix PPG sensor" → Next searches: "Goodix sensor specs", "Goodix competitors", "PPG sensor supply chain"

##### Contrarian Search & Source Quality

**Contrarian search**: When finding positive claims, search for opposing views to avoid echo chamber.
- Growth forecast → search "risks / challenges / bearish outlook"
- Product advantage → search "drawbacks / complaints / competitor strengths"

**Source quality** (prioritize high-quality sources for `web_fetch`):
| Source Type | Priority |
|---|---|
| Industry reports / financial analysis | ⭐⭐⭐⭐⭐ |
| Official announcements / Tech media | ⭐⭐⭐⭐ |
| Community (Reddit/YouTube) | ⭐⭐⭐ |
| SEO content / press releases | ⭐ |

##### Stop Conditions (Saturation Detection)

**✅ STOP when ANY condition met**:
- **Saturation**: No new entities/data for 2 consecutive rounds
- **Closure**: All initial unknowns filled and verified
- **Verification complete**: Key claims verified by 2+ sources

**❌ DO NOT stop when**: Important entities unexplored | Single-source claims | Unresolved contradictions

##### Minimum Requirements

| Metric | Minimum |
|---|---|
| web_search calls | 5+ |
| web_fetch calls | 8+ |
| Rounds | 2+ (until saturation) |
| Source types | 3+ (industry/news/community) |

**Comprehensive analysis: expect 15-30+ searches, 20-40+ web_fetch.**

#### Analysis Requirements

**Cross-source validation**: Connect insights from multiple sources to form structured judgments
- Example: "Google Trends shows 2x search growth + Amazon data shows $60-80 trail shoes have high ratings but limited supply + Alibaba shows only X suppliers = blue ocean opportunity with 40% margin potential"
- Example: "Search volume rising + Alibaba B2B has only X qualified suppliers = true blue ocean"

**Trend trajectory judgment**:
- State whether trend is emerging/explosive/mature/declining
- Identify if it's short-term hype or sustainable demand
- Note seasonality factors and 6-12 month outlook

**Trigger event analysis**: Identify sudden growth drivers (e.g., viral YouTube video, celebrity endorsement, KOL review, social media trend)

**Product categorization** (aggregate by sales/growth/reviews): The classification criteria are shown in the table below
| Category | Criteria | Recommendation |
|----------|----------|----------------|
| **Safe bets** | High sales + low complaints | Direct sourcing/imitation recommended |
| **High-potential** | High social buzz + low e-commerce supply | R&D opportunity |
| **Red ocean** | High sales + high complaints | Requires differentiation, cautious entry |
| **False trends** | Short-term spikes (holiday/event-driven) | Avoid long-term investment |

**Hit product feature extraction**:
- Analyze common traits of top-selling products
- Price band distribution and gaps (identify blank price zones)
- Must-have features vs. nice-to-have features
- Most complained features

**User pain point mining**: Extract insights from reviews using NLP analysis when data volume is sufficient

**Competitive landscape analysis**:
- Market concentration (top players' market share)
- Brand vs. white-label ratio
- Entry barriers and differentiation opportunities
- Pricing strategy patterns across competitors

**Supply chain feasibility assessment**:
- Identify "easy-entry variants" based on supplier availability
- MOQ and pricing structure analysis
- Supplier concentration and risk assessment
- Margin potential calculation (e.g., "B2B cost structure suggests 40% gross margin for white-label")

**Social & cultural trends**:
- Lifestyle shifts affecting demand
- Sustainability/ethical consumption preferences
- Regional/cultural preference variations
- Macro trends influencing category evolution

**Demand restatement**: Include 1-2 sentences restating your understanding of buyer needs

#### Output Requirements

**Text conclusions**: Include reasoning chains that connect data to insights.

**Visual evidence** (include whenever possible to enhance user understanding):
- **Images from search results**: When tool results return images with reference IDs, display 3-6 representative images that best illustrate the analysis (e.g., trending styles, product examples, market snapshots)
- **Trend visualization**: Show how trending styles/colors apply to products
  - Example: "Q4 best-seller is multi-function contouring stick" → show product images
  - Example: "Next season's trending color is lemon yellow" → show color palette + application across product categories
- **Code-generated charts** (prioritize consolidation and significance):
  - **Consolidate**: Draw related curves/metrics on a SINGLE chart for comparison (e.g., multiple trend lines on one plot)
  - **Simplicity**: Do NOT generate charts for simple information; use tables or text instead
  - **Types**: Trend charts (multi-line sales/search growth), Price distribution (histogram/box plot), Category comparison (multi-variable radar/bar), Word cloud (review keywords)
  - Use `matplotlib`, `seaborn`, or `wordcloud` to create professional visualizations
- **Hot-selling product display**:
  - Hot-selling products returned by `web_search` or `product_supplier_search`
  - You **MUST** display top hot-selling products using product card widgets
  - Display at least 3-5 top-performing hot-selling products to help users quickly identify market opportunities

**Comparison Table with Decision Notes** (MANDATORY when ≥3 candidates):

| Product/Category | Category | Key Metrics | Decision Note |
|------------------|----------|-------------|---------------|
| Smart Pet Feeder | High-potential | Social buzz rising, supply low | High-potential: precision feeding tech, note FDA certification |
| Pet Water Bottle | Safe bets | High sales, low complaints | Traffic-driver: EU regulation demand, note material shortage |
| Auto Litter Box | Red ocean | High sales, high complaints | Caution: needs differentiation, limit initial MOQ |

- **Category**: Classify each product using the 4 types (Safe bets/High-potential/Red ocean/False trends)
- **Decision Note**: `[positioning] + [evidence] + [risk/action]`

**Value explanation**: Use tangible examples to help users understand product value (e.g., "This multi-function beauty tool replaces 3 separate devices, saving counter space and $50")

**Case studies** (when available):
- Provide "industry gold standard" examples as benchmarks
- Real marketing/viral phenomena (social media trends, KOL reviews, successful brand strategies)
- Actionable "story lines" or content directions users can reference and adapt

**Source citations**:
- Link to original web pages and data sources
- Reference to source sales data tables with key metrics

**Selection Recommendation** (provide structured recommendations with three components):
- **Conclusion**: Specific products/categories grouped by strategy (Safe Bets, High-Potential, etc.)
- **Analysis Logic**: Framework used (e.g., "Blue Ocean Model") + key indicators (supply gap, social buzz, search trend, complaint ratio)
- **Data Evidence**: Verifiable sources (TikTok views, Amazon listings, Google Trends index, Alibaba supplier count)

---

### Step2: Product Search (Optional - SKIP unless user explicitly requests)

⚠️ **Trigger condition**: User explicitly asks for specific product cards/listings after seeing analysis results.

- **Input**: Selection recommendations from Step1
- **Action**: Search products using `product_supplier_search` (intent_type='product')
- **Output Requirements**:
  - ≥4 product cards per strategy dimension
  - **MUST display clickable cards** (not just text)
  - Comparison table for cross-evaluation

### Step3: Supplier Search & Inquiry (Optional - SKIP unless user explicitly requests)

⚠️ **Trigger condition**: User explicitly mentions supplier/manufacturer/factory/vendor keywords. "Find products" or "trending products" does NOT trigger this step.

- **Input**: User explicitly requests suppliers or inquiry
- **Action**: Search suppliers using `product_supplier_search` (intent_type='supplier'), assess suppliers, draft inquiry emails
- **Output**: Supplier recommendations + draft inquiry

### Step4: Summary & Action Guide (Mandatory)
- **Input**: All analysis completed
- **Action**: Synthesize findings into actionable recommendations
- **Output**: Executive summary table, opportunities/risks, specific next steps

---

# Output Format

### Analysis Output Structure

```
1. Demand Understanding
  - Restate user needs (1-2 sentences)

2. Market Analysis & Selection Recommendation (Step1)
  - Text conclusions with reasoning chains
  - Visual evidence (images, charts, hot-selling product cards)
  - Comparison Table with Decision Notes (categorization + decision note per product)
  - Selection recommendation (conclusion + analysis logic + data evidence)

3. Product Cards (Step2, only if user explicitly requests products)
  - Structured by recommendation dimension
  - ≥4 clickable cards per strategy dimension
  - Comparison table for cross-evaluation

4. Supplier Options (Step3, only if user explicitly requests suppliers)

5. Summary & Action Guide (Step4)
  - Opportunities & risks table
  - Specific next steps
  - Data limitations

6. Next Task Suggestions (if applicable)
```

### Chart Design

**Prefer charts when you have enough data**: if the dataset is sufficiently rich (≥5 data points or ≥3 categories), include charts to improve clarity. The more data you have, the more you should rely on charts (and less on pure long text).

**⚠️ CRITICAL: If you need to generate ANY chart, you MUST read the chart design guide FIRST**:
- **File path**: `./chart-design-guide.md` (relative to this SKILL.md)
- **Why**: Charts generated WITHOUT reading this guide will have poor styling, wrong chart types, and deprecated API usage
- **Contains**: Tool selection (Seaborn vs Matplotlib), code patterns, styling, chart type selection, Seaborn v0.12+ API updates

---

# Checklist

✅ **Strongly Recommended**:
- [ ] For broad category questions: define segment + timeframe first (Demand & Scope), then build the candidate pool
- [ ] For specific products: include concrete product inputs (image, price, link/ID, variant) and do a full deep dive (not a quick verdict)
- [ ] Use multi-source evidence: YouTube + Reddit (VoC) + at least one trend/marketplace signal
- [ ] Always output **Trend Candidate List → Top Picks → Deep Dive Packs → Final Recommendation**
- [ ] For specific products: build a **Specific Product Evidence Pack** (listing + competitors + VoC + momentum) before writing "reasons"
- [ ] Use scenario routing (Red/Blue/Ghost) to decide validation depth
- [ ] In Red Ocean: base differentiation on recurring, fixable mixed-sentiment pain point patterns (benefit + complaint)
- [ ] Include sources in the final output

❌ **Avoid**:
- Treating all products with the same depth (violates "adaptive")
- Ignoring VoC (reviews/comments)
- Listing data without a decision rule