From venture-capital-intelligence
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.
npx claudepluginhub isanthoshgandhi/venture-capital-intelligenceThis skill uses the workspace's default tool permissions.
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.
Guides TDD-style skill creation: pressure scenarios as tests, baseline agent failures, write docs to enforce compliance, verify with RED-GREEN-REFACTOR.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Guides idea refinement into designs: explores context, asks questions one-by-one, proposes approaches, presents sections for approval, writes/review specs before coding.
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
Ask for or extract:
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:
Save to ${CLAUDE_PLUGIN_ROOT}/skills/market-size/output/market_inputs.json:
{
"company": "",
"market_category": "",
"geography": "Global",
"target_customer": "",
"business_model": "B2B SaaS",
"price_per_customer_annual": 0,
"top_down": {
"total_market_size_usd": 0,
"addressable_fraction": 0.0,
"obtainable_fraction": 0.0,
"cagr_pct": 0.0,
"source": ""
},
"bottom_up": {
"total_potential_customers": 0,
"addressable_customers": 0,
"obtainable_customers": 0,
"arpu_annual": 0
},
"competitors": [
{
"name": "",
"funding_total_usd": 0,
"estimated_arr_usd": 0,
"founded_year": 0,
"differentiation": ""
}
]
}
Estimation guidance:
bottom_up_TAM = total_customers × ARPURun: python "${CLAUDE_PLUGIN_ROOT}/skills/market-size/scripts/tam_calculator.py"
Computes both methods and derives a consensus range. Flags if TAM < $1B (below venture threshold).
For each major competitor, identify their technology stack based on:
Classify each competitor's stack using the webappanalyzer taxonomy:
This reveals: technical maturity, rebuild risk, hiring difficulty, and migration complexity for enterprise customers.
Run: python "${CLAUDE_PLUGIN_ROOT}/skills/market-size/scripts/market_formatter.py"
After computing, flag: