Agent skill

Market Analyzer

by Mathews-Tom in Mathews-Tom/armory

Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports.

MITAuto-check passedProduct & Project Management

Install Market Analyzer

skills CLI
$ npx skills add Mathews-Tom/armory --skill market-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Mathews-Tom/armory market-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/market-analyzer .claude/skills/market-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
market-analyzer
GitHub stars
329
Token cost
~2.4k tokens
SKILL.md length
760 words
Files
5 (incl. references)
Skills in repo
80
Repo updated
First seen
Licence
MIT

At a glance

Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports.

  • Works in 6 steps: Input Understanding → Market Research → TAM/SAM/SOM Calculation → …
  • Market opportunity
  • SKILL.md covers Reference Files, Prerequisites, Workflow and Output Format, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Market Analyzer is an agent skill from Mathews-Tom/armory. 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.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `evals/cases.yaml`, `references/market-sizing.md` and `references/tam-sam-som.md`).

It sits in Product & Project Management, covering Market research, Market sizing and Startup and business strategy. The repository describes itself as: Curated, production-grade skills for AI coding agents. Battle-tested workflows for developers who use AI seriously. The licence is MIT.

When your agent uses it

  • Market opportunity
  • Industry analysis
  • How big is the market

Example prompts

  • “market size”
  • “TAM SAM SOM”
  • “market opportunity”
  • “/market-analyzer”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Input Understanding
  2. Market Research
  3. TAM/SAM/SOM Calculation
  4. Trend and Timing Assessment
  5. Customer Segmentation
  6. Report Generation

What it can do on your machine

Read from SKILL.md and the folder at commit 4594fb7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Market Analyzer loads about 2.4k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Mathews-Tom/armory at commit 4594fb7, republished under its MIT licence (© Mathews-Tom). 760 words, ~2,434 tokens.

Download SKILL.mdSave it as .claude/skills/market-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
market-analyzer
description
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.
metadata.version
1.0.1
metadata.category
research
metadata.tags
market-sizing, tam-sam-som, trends, industry
metadata.difficulty
intermediate
metadata.phase
define

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.}
Show full SKILL.md (325 more words)Show less

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

© Mathews-Tom, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/market-analyzer of Mathews-Tom/armory.

  • SKILL.md
  • evals/cases.yaml
  • references/market-sizing.md
  • references/tam-sam-som.md
  • references/trend-analysis.md

Open the folder on GitHubat commit 4594fb7

Compare with similar skills

Market Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Market Analyzer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Market Analyzer this skillMathews-Tom/armory329—~2.4kAutomated safety check: PassMIT
Market Sizing Analysiswshobson/agents40k1 repos~620Automated safety check: PassMIT
Market Researchshawnpang/startup-founder-skills343—~1.9kAutomated safety check: PassMIT
Startup Designferdinandobons/startup-skill1.2k—~8.1kAutomated safety check: PassMIT
Market Research Analysismanojbajaj95/claude-gtm-plugin105—~2.6kAutomated safety check: PassMIT
Startup Analystaiskillstore/marketplace4339 repos~2.9kAutomated safety check: PassNone

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Questions about Market Analyzer

What does Market Analyzer do?

Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports. Market Analyzer is an agent skill from Mathews-Tom/armory. Structured market analysis with TAM/SAM/SOM sizing, trends, and competitive landscape via WebSearch, producing investor-grade cited reports.

When should I use Market Analyzer?

Market Analyzer fits situations like: market opportunity; industry analysis; how big is the market.

How do I install Market Analyzer in Claude Code?

Run `npx skills add Mathews-Tom/armory --skill market-analyzer -a claude-code`. Or copy the skill folder (skills/market-analyzer in Mathews-Tom/armory) into .claude/skills/market-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Market Analyzer in Codex?

Run `npx skills add Mathews-Tom/armory --skill market-analyzer -a codex`. Or copy the skill folder (skills/market-analyzer in Mathews-Tom/armory) into .agents/skills/market-analyzer in your project. Codex loads it when a task matches its description.

Can I use Market Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mathews-Tom/armory --skill market-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/market-analyzer, .gemini/skills/market-analyzer, .github/skills/market-analyzer and .opencode/skills/market-analyzer in your project.

What does Market Analyzer need to run?

SKILL.md names no scripts, command-line tools or credentials: Market Analyzer is instructions for the agent only.

Does Market Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Market Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Market Analyzer use?

Market Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Market Analyzer use?

About 2.4k tokens (SKILL.md is roughly 9.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.5k tokens, read only when the agent opens those files.

What are the alternatives to Market Analyzer?

Skills that share tags, products or a category with Market Analyzer: Market Sizing Analysis (wshobson/agents, 40k stars), Market Research (shawnpang/startup-founder-skills, 343 stars), Startup Design (ferdinandobons/startup-skill, 1.2k stars) and Market Research Analysis (manojbajaj95/claude-gtm-plugin, 105 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Market Analyzer?

Mathews-Tom (a GitHub user) maintains it in Mathews-Tom/armory, which has 329 GitHub stars. The repository holds 80 skills in this directory. The repository was last updated on October 6, 2026.

Source: Mathews-Tom/armory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.