market-size
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.
Best use case
market-size is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using market-size should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/market-size/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How market-size Compares
| Feature / Agent | market-size | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 riskRelated Skills
soft-screening-startup
Activate for ANY startup evaluation, investment screening, or company assessment. Triggers include: "evaluate this startup", "screen this company", "should I invest in X", "is this a good investment", "what do you think about this company", "review this startup", "score this company", "rate this pitch", "assess this founder", "quick take on X", "is X worth investing in", "pass or decline on X", "what's your verdict on X", "first look at this company", "quick screen on X", "what's your take on this founder", "is this fundable", "would a VC invest in this". Also triggers when a user pastes a company description, funding ask, or founder background and asks for an opinion. Works on claude.ai and Claude Code. For hard-mode deterministic scoring with Python audit trail, use /venture-capital-intelligence:hard-screening-startup.
hard-screening-startup
Deterministic Python-scored startup screening with full audit trail. Use when you need a reproducible, weighted-score verdict on a startup — not just a qualitative opinion. Triggered by: "/venture-capital-intelligence:hard-screening-startup", "hard screen this startup", "run a hard screen on X", "score this startup with Python", "give me an auditable screen", "run a scored evaluation on X", "give me a weighted score for this startup", "screen with numbers", "objective startup score", "reproducible screen", "investment scorecard for X", "score this company out of 100", "run the full screen on X". Claude Code only. Requires Python 3.x. For conversational soft-mode screening, use /venture-capital-intelligence:soft-screening-startup.
fund-operations
Compute fund KPIs (TVPI, DPI, IRR, MOIC), model carried interest and management fees, and generate LP quarterly update narratives. Triggered by: "/venture-capital-intelligence:fund-operations", "calculate fund KPIs", "what is my fund TVPI", "IRR calculation", "compute MOIC", "LP report", "quarterly update draft", "carried interest calculation", "management fee calculation", "fund performance report", "write my LP update", "how is my fund performing", "what is my DPI", "fund returns analysis", "model my carry", "how much carry do I earn", "portfolio performance summary", "generate investor update". Claude Code only. Requires Python 3.x.
financial-model
Run deterministic financial models for startup valuation and SaaS health analysis. Triggered by: "/venture-capital-intelligence:financial-model", "run a financial model on X", "DCF this company", "model the financials", "calculate runway", "what is the valuation", "SaaS metrics model", "LTV CAC analysis", "unit economics", "burn rate analysis", "comparable valuation", "how long is my runway", "what's my burn multiple", "revenue projection for X", "model the ARR growth", "what is the pre-money valuation", "comps analysis", "NRR and churn model", "how healthy are these SaaS metrics". Claude Code only. Requires Python 3.x. Accepts user-supplied numbers or searches for publicly available data.
explain-equity-terms
Activate for ANY equity, legal, or term sheet question related to startup investing or fundraising. Triggers include: "what is a SAFE", "explain this term sheet", "what does pro-rata mean", "what is liquidation preference", "explain anti-dilution", "ISO vs NSO", "what is a 83(b) election", "what is carried interest", "explain drag-along", "what is a valuation cap", "what does MFN mean", "explain convertible note vs SAFE", "what is a down round", "explain vesting cliff", "what does fully diluted mean", "term sheet question", "equity question", "what does this clause mean". Also triggers when a user pastes legal text from a term sheet, SAFE, or subscription agreement and asks what it means. Works on claude.ai and Claude Code.
deal-sourcing-signals
Scan a company or sector for deal-sourcing signals across 6 dimensions. Triggered by: "/venture-capital-intelligence:deal-sourcing-signals", "scan signals for X", "what signals is X showing", "deal sourcing scan", "hiring signals for X", "is X raising soon", "monitor this company", "company signal scan", "sourcing brief for X", "what is X up to", "is X growing", "track this company", "deal signal report for X", "is this company fundraising", "what are the momentum signals for X", "find signals on X", "is X worth tracking". Claude Code only. Requires Python 3.x. Uses web search for live signal data.
cap-table-waterfall
Model cap table dilution, SAFE conversion, and exit waterfall across scenarios. Triggered by: "/venture-capital-intelligence:cap-table-waterfall", "model my cap table", "simulate dilution", "SAFE conversion math", "exit waterfall", "how much do I own after Series A", "liquidation waterfall", "cap table scenario", "what happens to equity at exit", "model the waterfall", "how much equity do I have left", "what is my ownership after funding", "run dilution scenarios", "model a new round", "what happens at acquisition", "cap table after SAFE conversion", "pari passu waterfall", "preference stack analysis". Claude Code only. Requires Python 3.x.
analyze-pitch-deck
Activate for ANY pitch deck analysis, feedback, or review request. Triggers include: "analyze this deck", "review my pitch deck", "critique my pitch", "feedback on my slides", "is my deck investor ready", "what's wrong with my pitch", "how would a VC react to this deck", "score my pitch deck", "rate my slides", "improve my deck", "what slides am I missing", "is this pitch compelling". Also triggers when a user pastes slide content, describes their deck structure, or shares a company narrative and asks for investor feedback. Works on claude.ai and Claude Code.
public-plugin-builder
Activate when the user wants to build a Claude plugin, create a Claude skill, make a Claude agent, structure a Claude Code plugin, says "build a plugin", "create a skill", "new claude skill", "new agent", "help me make a plugin", "plugin builder", "claude plugin helper", "how do I build a Claude skill", "I want to create a Claude plugin", "plugin building", or asks how to structure a Claude Code plugin or publish to the Claude marketplace. Works on both claude.ai (generates files as code blocks) and Claude Code (writes and pushes files).
server-components
This skill should be used when the user asks about "Server Components", "Client Components", "'use client' directive", "when to use server vs client", "RSC patterns", "component composition", "data fetching in components", or needs guidance on React Server Components architecture in Next.js.
server-actions
This skill should be used when the user asks about "Server Actions", "form handling in Next.js", "mutations", "useFormState", "useFormStatus", "revalidatePath", "revalidateTag", or needs guidance on data mutations and form submissions in Next.js App Router.
route-handlers
This skill should be used when the user asks to "create an API route", "add an endpoint", "build a REST API", "handle POST requests", "create route handlers", "stream responses", or needs guidance on Next.js API development in the App Router.