Market Scanner

Scan and map your market landscape — size, structure, trends, and seasonality. Uses Perplexity for deep market research. Output feeds every downstream research and marketing skill.

Purpose

Market Scanner is the starting point of all research. It answers:

• What market are we actually in? (precise category, not vague industry)
• How big is it, and where is it headed?
• What macro forces are shaping it?
• How is the market structured?
• When does demand peak and trough?

The output — research-memory/market-landscape.md — becomes the shared context that every subsequent skill (competitor-finder, audience-profiler, brand-voice, SEO, copy, etc.) reads to stay grounded in market reality.

“Doing marketing without research is like driving without a map.” — The Boring Marketer

Modes

Auto-Load Protocol

On every invocation, BEFORE any research:

1. Check research-memory/ directory
2. If files exist → Read ALL .md files (except README.md)
3. Use loaded context to:
   • Skip already-covered areas (avoid redundancy)
   • Build on existing findings (deepen, not repeat)
   • Maintain consistency with other research outputs
4. Check brand-memory/ (read-only) → If exists, use business description, positioning, and audience info as input context
5. If market-landscape.md has data AND research-log.md shows a previous scan → suggest Refresh mode

Input Gathering

Collect from the user conversationally. Do NOT dump a form — ask naturally.

If brand-memory/ exists, pre-fill from voice-profile.md and positioning.md — ask user to confirm or correct.

If this is a Refresh, show the current market-landscape.md summary and ask: “What has changed or what do you want to update?”

Research Intensity

사용자가 명시적으로 요청하면 리서치 깊이를 조절합니다. 지정하지 않으면 deep (기본값).

“가볍게”, “빠르게”, “간단히” → light / “보통으로”, “적당히” → standard / 별도 지정 없음 → deep

Process

Step 1: Define Market Category

Goal: Pin down the PRECISE market category — not “software” but “AI-powered marketing automation for SMBs.”

Tool: perplexity_reason (requires analytical reasoning)

Query pattern:

What is the precise market category and sub-segment for a business that [user's business description]?

Define:
- The specific market segment (not broad industry)
- Adjacent segments that partially overlap
- Where the boundaries are (what's IN vs OUT of this market)

Be specific: "DTC home textiles" not "home goods"

Parameters:

• search_context_size: Research Intensity에 따라 결정 (light→”low” / standard→”medium” / deep→”high”)

Output: Category name, scope definition, boundary clarification, adjacent markets.

Step 2: Market Size & Growth (TAM / SAM)

Goal: Quantify the market opportunity with credible numbers.

Tool: perplexity_reason (numerical reasoning required)

Query pattern:

What is the current market size for [market category from Step 1]?

Provide:
1. TAM (Total Addressable Market) — global value
2. SAM (Serviceable Addressable Market) — the realistic slice for [business type/geography]
3. CAGR projection for the next 3-5 years
4. Key regional breakdown (if relevant)

For each number: cite the source name and year of data.
If multiple sources conflict, note the range.

Parameters:

• search_context_size: Research Intensity에 따라 결정 (light→”low” / standard→”medium” / deep→”high”)
• search_recency_filter: “year”

Output: TAM, SAM, CAGR, regional split — all with source citations.

Step 3: Macro Trends + Opportunity/Threat Tagging

Goal: Identify 3-5 macro forces shaping this market, and tag each as opportunity or threat.

Tool: perplexity_ask (fact-based, current data)

Query pattern:

What are the top 3-5 macro trends shaping the [market category] market in [current year]?

For each trend, cover:
- Category: consumer behavior / technology / regulation / economic / cultural
- Description: what's happening and why it matters
- Impact on a [business type]: is this an OPPORTUNITY or THREAT?
- Timeframe: immediate (0-1yr), near-term (1-3yr), or long-term (3-5yr)
- Evidence: cite recent data points or examples

Parameters:

• search_recency_filter: “month”
• search_context_size: Research Intensity에 따라 결정 (light→”low” / standard→”medium” / deep→”high”)

Output: 트렌드 수는 intensity에 따라 결정 (light=2-3 / standard=3 / deep=3-5), 각 트렌드에 category, opportunity/threat, timeframe, evidence 포함.

Step 4: Market Structure Map + Seasonality

Goal: Understand how the market is organized and when demand peaks/troughs.

Tool: perplexity_ask

Query pattern:

How is the [market category] market structured?

Map along these dimensions:
1. Price tiers: budget / mid-range / premium / luxury
2. Distribution channels: DTC, retail, marketplace, wholesale, SaaS, etc.
3. Customer segments: demographics, use cases, geography
4. Product sub-categories: major product/service lines

Also identify:
- Seasonal demand patterns (peak months, trough months)
- Event-driven demand spikes (holidays, industry events, budget cycles)
- Any cyclical patterns (quarterly, annual)

Parameters:

• search_context_size: Research Intensity에 따라 결정 (light→”low” / standard→”medium” / deep→”high”)

Output: Market structure 분석 범위는 intensity에 따라 결정 (light=2차원 핵심만 / standard=3차원 / deep=4차원 전체) + seasonality calendar.

Step 5: Save & Log

Goal: Write all findings to research-memory/market-landscape.md and log the execution.

5a. Write market-landscape.md

Language rule: 섹션 헤더와 테이블 컬럼명은 영어로 유지합니다. 본문, 셀 값, 설명, 분석 텍스트는 사용자가 지정한 언어로 작성합니다. 언어가 지정되지 않으면 English로 작성합니다.

Use the exact schema below. Fill every section with Step 1-4 findings.

# Market Landscape
> Last updated: [YYYY-MM-DD]
> Source skill: market-scanner

## Market Definition
[Category name, scope, boundaries, adjacent markets]

## Market Size (TAM / SAM)
| Metric | Value | Source | Year |
|--------|-------|--------|------|
| TAM | $XXB | [source] | [year] |
| SAM | $XXB | [source] | [year] |
| Growth Rate (CAGR) | XX% | [source] | [year range] |

[Regional breakdown if relevant]

## Growth Trajectory
[Growth stage: early / growth / mature / declining]
[Key growth drivers]
[Headwinds / risks]

## Macro Trends
| # | Trend | Category | Type | Impact | Timeframe |
|---|-------|----------|------|--------|-----------|
| 1 | [trend] | [category] | Opportunity/Threat | [impact] | [timeframe] |
| 2 | [trend] | [category] | Opportunity/Threat | [impact] | [timeframe] |
| 3 | [trend] | [category] | Opportunity/Threat | [impact] | [timeframe] |

## Market Structure Map
### By Price Tier
[budget / mid / premium / luxury breakdown]

### By Channel
[DTC, retail, marketplace, etc.]

### By Customer Segment
[demographics, use cases, geography]

### By Sub-Category
[product/service lines]

## Seasonality & Demand Cycles
[Peak months, trough months, event-driven spikes]
[Budget cycles, quarterly patterns]

For Refresh mode: Do NOT overwrite the entire file. Update only the sections that changed. Append > Updated: [date] below the section header for each changed section.

5b. Update research-log.md

Append one row to the log:

| [YYYY-MM-DD] | market-scanner | Full Scan / Refresh | [brief summary of key findings or changes] | Perplexity |

Perplexity MCP Tool Guide

Common parameters:

• search_context_size: Research Intensity 레벨에 따라 결정 — 위 Research Intensity 테이블 참조
• search_recency_filter: "month" for trends (Step 3), "year" for size data (Step 2)

Query best practices:

• Be specific: Include the exact market category in every query
• Ask for sources: Always request citation of source name + data year
• Handle conflicts: When sources disagree, report the range, not a single number
• One topic per query: Don’t combine market size + trends in one call
• Language: 사용자가 English 외 언어를 지정한 경우, 모든 query 끝에 “Respond in [language].”를 추가

Quality Checklist

Before saving, verify:

• Market category is PRECISE (not “tech” but “AI-powered marketing automation for SMBs”)
• TAM/SAM numbers have source citations and data year
• 매크로 트렌드 최소 2개 (light) ~ 5개 (deep) 식별, 각각 opportunity/threat 태그 포함
• Market structure 최소 2차원 (light) ~ 4차원 (deep) 커버 (price, channel, segment, sub-category)
• Seasonality section is populated (even if “no strong seasonality detected”)
• All Perplexity responses include source URLs in the output
• research-log.md updated with execution record

Example (Abbreviated)

Input: “Marketing education business selling skill packs and courses to solo marketers.”

Category: Creator Economy — Marketing Education & Digital Skills TAM: $30B (global online education, marketing subset — HolonIQ 2024) SAM: $3B (English-speaking solo marketers — estimated 2024) CAGR: 12-15% (SignalFire, 2024-2029) Trends: AI democratizing execution (Opportunity), Creator economy growth (Opportunity), AI replacing traditional education (Threat) Seasonality: Q1 peak (New Year), Q4 trough (holidays), Sep mini-peak (back-to-work)

What This Skill Does NOT Do

• Competitor analysis → Use competitor-finder (separate skill)
• Customer profiling → Use audience-profiler (separate skill)
• Customer language mining → Use voice-of-customer (separate skill)
• Strategic recommendations → Use research-synthesizer (reads this output)

Market Scanner stays focused on the market itself — size, shape, forces, timing.