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Market Analyzer

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Mathews-Tom
market-analyzer

Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports. Triggers on: "market size", "TAM SAM SOM", "market opportunity", "industry analysis", "how big is the market", "market trends". NOT for financial modeling or pricing.

Overview

PublisherMathews-Tom
Repositoryarmory
Skill namemarket-analyzer
Stars
318
Forks
47
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Market Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/market-analyzer .claude/skills/market-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Market Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Market Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Market Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Market Analyzer

Produces structured market analysis reports: sizes the addressable market using top-down and bottom-up methods, identifies growth trajectories and adoption stages, segments customers, and assesses timing. Every estimate cites a data source or states its assumption explicitly.

Reference Files

FileContentsLoad When
references/tam-sam-som.mdTAM/SAM/SOM definitions, calculation formulas, common mistakesAlways
references/trend-analysis.mdTrend categorization framework, adoption lifecycle, timing signalsAlways
references/market-sizing.mdData sources, estimation techniques, confidence frameworkAlways

Prerequisites

  • Product, feature, or business idea description
  • Target geography (default: global)
  • Target customer segment (if known)

Workflow

Phase 1: Input Understanding

  1. Classify the input — Determine whether the subject is a product idea, feature, market category, or industry vertical.
  2. Extract key attributes:
    • Target customer profile
    • Value proposition or core problem solved
    • Geography and regulatory jurisdiction
    • Price range or monetization model (if known)
    • Competitive alternatives
  3. Clarify gaps — If critical context is missing (target customer, geography, or value proposition), ask 3-5 targeted clarifying questions before proceeding.

Phase 2: Market Research

Use WebSearch to gather quantitative market data:

  1. Industry reports — Search for market size reports from Statista, Grand View Research, Fortune Business Insights, IBISWorld, and similar aggregators.
  2. Growth rates — Identify CAGR (Compound Annual Growth Rate) for the relevant market and adjacent segments.
  3. Funding signals — Search Crunchbase, PitchBook coverage, and venture capital trends in the space.
  4. Consumer trends — Google Trends data, social media volume, and news sentiment.
  5. Academic research — Search Semantic Scholar for relevant market studies, adoption research, and behavioral economics papers.
  6. Government/public data — Census Bureau, BLS, World Bank, OECD datasets for demographic and economic baselines.

For each data point, record the source, publication date, and methodology (if available).

Phase 3: TAM/SAM/SOM Calculation

Apply both estimation approaches and cross-validate:

Top-down (from total industry):

TAM = Total industry revenue or total potential buyers x average revenue per buyer
SAM = TAM x % addressable by geography, segment, and channel
SOM = SAM x realistic capture rate (year 1-3)

Bottom-up (from unit economics):

Reachable customers = Identified target accounts or users in reachable channels
SOM = Reachable customers x conversion rate x average revenue per customer
SAM = SOM scaled to full serviceable segment (remove channel constraints)
TAM = SAM scaled to total market (remove geographic/segment constraints)

Cross-validate the two approaches. If they diverge by more than 3x, investigate the discrepancy and document the reason.

Phase 4: Trend and Timing Assessment

Evaluate four dimensions:

  1. Market growth trajectory — Classify as emerging (pre-revenue), growing (CAGR > 10%), mature (CAGR 0-5%), or declining (negative CAGR).
  2. Technology adoption stage — Map to Rogers curve: innovators (< 2.5%), early adopters (2.5-16%), early majority (16-50%), late majority (50-84%), laggards (> 84%).
  3. Regulatory environment — Identify tailwinds (subsidies, mandates) and headwinds (restrictions, compliance costs).
  4. Macro trends — Economic conditions, demographic shifts, technological enablers that accelerate or hinder the market.

Phase 5: Customer Segmentation

Identify 2-5 distinct customer segments:

  • Demographics — Age, income, geography, company size (B2B)
  • Behavioral — Usage patterns, purchase triggers, switching costs
  • Willingness to pay — Price sensitivity signals, competitive pricing data
  • Segment sizing — Estimated size and growth rate per segment

Phase 6: Report Generation

Produce the structured output below.

Output Format

text
## Market Analysis: {Subject}

### Executive Summary
**Market Opportunity Score: {1-5}/5**
{2-3 sentence summary of the opportunity, key market size, and timing assessment.}

### TAM / SAM / SOM

| Level | Value | Methodology | Confidence |
|-------|-------|-------------|------------|
| TAM | ${amount} | {Top-down / Bottom-up / Both} | {High/Medium/Low} |
| SAM | ${amount} | {methodology summary} | {High/Medium/Low} |
| SOM (Year 1) | ${amount} | {methodology summary} | {High/Medium/Low} |
| SOM (Year 3) | ${amount} | {methodology summary} | {High/Medium/Low} |

**Top-down calculation:**
{Step-by-step derivation with sources}

**Bottom-up calculation:**
{Step-by-step derivation with sources}

**Cross-validation:**
{Comparison of approaches, explanation of any divergence}

### Market Trends

| Dimension | Assessment | Evidence |
|-----------|-----------|----------|
| Growth trajectory | {Emerging/Growing/Mature/Declining} | {CAGR, data source} |
| Adoption stage | {Innovators/Early Adopters/Early Majority/Late Majority} | {penetration %, signal} |
| Regulatory | {Tailwind/Neutral/Headwind} | {specific regulation or policy} |
| Macro trends | {Favorable/Mixed/Unfavorable} | {key trend} |

### Customer Segments

| Segment | Size | Growth | WTP Signal | Priority |
|---------|------|--------|------------|----------|
| {name} | {size} | {rate} | {signal} | {Primary/Secondary/Tertiary} |

### Key Risks and Assumptions

| # | Assumption | Impact if Wrong | Confidence |
|---|-----------|-----------------|------------|
| 1 | {assumption} | {impact} | {High/Medium/Low} |

### Data Quality Assessment

| Data Point | Source | Date | Quality |
|-----------|--------|------|---------|
| {metric} | {source} | {date} | {Verified/Estimated/Extrapolated} |

### Recommendation
{1-2 paragraphs: proceed/pivot/investigate further, with specific next steps.}

Scoring Criteria: Market Opportunity Score

ScoreMeaningCriteria
5ExceptionalLarge TAM (> $10B), growing (> 15% CAGR), early adoption stage, regulatory tailwinds
4StrongLarge TAM or high growth, favorable timing, manageable competition
3ModerateMid-size market, moderate growth, competitive but differentiation possible
2ChallengingSmall or saturated market, mature stage, significant headwinds
1UnfavorableDeclining market, regulatory barriers, limited differentiation

Quality Rules

  1. Every number needs a source. Cite the report, database, or methodology used. If no source exists, label the estimate as "Author extrapolation" and state the assumption chain.
  2. Distinguish data from extrapolation. Use the Data Quality Assessment table to make this explicit for every key metric.
  3. Confidence levels are mandatory. Each TAM/SAM/SOM figure carries a confidence rating with rationale.
  4. Cross-validate estimates. Run both top-down and bottom-up. If only one approach is feasible, state why and reduce confidence.
  5. Date your data. Market data older than 3 years gets a lower confidence rating. Flag any pre-2022 data explicitly.
  6. No vanity TAMs. The TAM must be genuinely addressable by the product category, not inflated by including tangential markets.

Error Handling

ProblemResolution
No market data availableUse proxy markets and analogies. State the proxy explicitly. Reduce confidence to Low.
Input too vague to sizeAsk clarifying questions (target customer, geography, price point) before proceeding.
Conflicting data sourcesPresent both figures, explain the discrepancy, use the more conservative estimate.
Market is too new for reliable dataSize the adjacent market the product displaces. Note the nascent stage.
User wants a single TAM numberProvide the range (conservative to optimistic) with the methodology behind each bound.

When NOT to Analyze

Push back if:

  • The request is for financial projections or revenue forecasting (different skill domain)
  • The request is for pricing strategy or competitive positioning (strategy, not analysis)
  • The market definition is so broad it has no analytical value ("the internet economy")
  • The user has not defined what the product or idea actually does

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Market Analyzer AI skill do?

Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports. Triggers on: "market size", "TAM SAM SOM", "market opportunity", "industry analysis", "how big is the market", "market trends". NOT for financial modeling or pricing.

Why use Market Analyzer on TypingMind?

Because you install it once and use it with any model. Market Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Market Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/market-analyzer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Market Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Market Analyzer?

As many as you like. As long as a model supports skills, you can use Market Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Market Analyzer AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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