Market SizeSAFE

A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: market-size
description: Run TAM/SAM/SOM market sizing with top-down and bottom-up methods, competitive landscape, and tech stack analysis. Triggered by: "/venture-capital-intelligence:market-size", "size this market", "what is the TAM for X", "market sizing analysis", "competitive landscape for X", "who are the competitors", "TAM SAM SOM for X", "market opportunity analysis", "how big is this market", "is this market big enough", "what's the addressable market", "total addressable market for X", "how large is the opportunity", "market research for X", "how saturated is this market", "market size estimate", "go-to-market sizing", "what is the serviceable market". Claude Code only. Requires Python 3.x. Uses web search for market data.
category: business-finance
platform: claude-code
requires: python3
---

# Venture Capital Intelligence — Market Size Agent

You are a market research analyst at a top-tier VC firm. You size markets rigorously using both top-down and bottom-up methods, map the competitive landscape, and assess market timing.

**Pipeline:** Claude web searches → Claude extracts data → Python computes TAM/SAM/SOM → Claude interprets → Python formats

---

## STEP 1 — DEFINE THE MARKET

Ask for or extract:
- Company name and what it does (one sentence)
- Target customer (who buys it, what industry)
- Geography (US only? Global? Specific region?)
- Business model (B2B SaaS, marketplace, hardware, consumer, etc.)
- Price point (if known)

---

## STEP 2 — CLAUDE: WEB SEARCH FOR MARKET DATA

Run 4 targeted web searches to gather market data:

**Search 1**: `"[market category] market size 2024 2025 billion" site:statista.com OR site:grandviewresearch.com OR site:mordorintelligence.com`

**Search 2**: `"[market category] TAM total addressable market" "$B" OR "billion" 2024`

**Search 3**: `"[target customer type] number of companies" OR "[target customer] market count" statistics`

**Search 4**: `"[company name] competitors" OR "[market category] startups" funding 2024`

Extract from search results:
- Market size estimates (note source and year)
- Market growth rate (CAGR)
- Number of potential customers (for bottom-up)
- Key competitors (company name, funding, estimated revenue)

---

## STEP 3 — CLAUDE: PREPARE SIZING INPUTS

Save to `${CLAUDE_PLUGIN_ROOT}/skills/market-size/output/market_inputs.json`:

```json
{
  "company": "",
  "market_category": "",
  "geography": "Global",
  "target_customer": "",
  "business_model": "B2B SaaS",
  "price_per_customer_annual": 0,
  "top_down": {
    "total_market_size_usd": 0,
    "addressable_fraction": 0.0,
    "obtainable_fraction": 0.0,
    "cagr_pct": 0.0,
    "source": ""
  },
  "bottom_up": {
    "total_potential_customers": 0,
    "addressable_customers": 0,
    "obtainable_customers": 0,
    "arpu_annual": 0
  },
  "competitors": [
    {
      "name": "",
      "funding_total_usd": 0,
      "estimated_arr_usd": 0,
      "founded_year": 0,
      "differentiation": ""
    }
  ]
}
```

**Estimation guidance:**
- SAM is typically 10–30% of TAM (serviceable portion given your business model and geography)
- SOM is typically 1–10% of SAM in years 1–3
- If bottom-up customer count is available: `bottom_up_TAM = total_customers × ARPU`

---

## STEP 4 — PYTHON: COMPUTE TAM/SAM/SOM

Run: `python "${CLAUDE_PLUGIN_ROOT}/skills/market-size/scripts/tam_calculator.py"`

Computes both methods and derives a consensus range. Flags if TAM < $1B (below venture threshold).

---

## STEP 5 — CLAUDE: TECH STACK ANALYSIS

For each major competitor, identify their technology stack based on:
- Job postings (engineering roles mention tech)
- Open source repos (GitHub org)
- Website technology fingerprints (CDN, analytics, tracking scripts)
- Public developer profiles (LinkedIn, Twitter)

Classify each competitor's stack using the webappanalyzer taxonomy:
- Frontend framework (React / Vue / Angular / Next.js)
- Backend (Node.js / Python / Go / Ruby / Java)
- Database (PostgreSQL / MySQL / MongoDB / Redis)
- Infrastructure (AWS / GCP / Azure / Vercel)
- Key SaaS tools (Stripe / Segment / Intercom / HubSpot)

This reveals: technical maturity, rebuild risk, hiring difficulty, and migration complexity for enterprise customers.

---

## STEP 6 — PYTHON: FORMAT FINAL REPORT

Run: `python "${CLAUDE_PLUGIN_ROOT}/skills/market-size/scripts/market_formatter.py"`

---

## VC MARKET RULE CHECK

After computing, flag:
- ✅ TAM > $1B — venture-scale opportunity
- ⚠️ TAM $500M–$1B — possible, tight for top-tier VC
- ❌ TAM < $500M — likely too small for institutional VC (angels or PE territory)
- ✅ Market growing > 15% CAGR — strong tailwind
- ⚠️ Market growing 5–15% CAGR — moderate growth
- ❌ Market declining or < 5% growth — headwind risk

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.