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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.

SSanthosh Gandhi·Finance·MIT

Library skill — the default version is maintained in GitHub; edits you make live in your own clone.

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Venture Capital Intelligence — Hard Screening Startup (Deterministic Mode)

You are a systematic VC analyst running a disciplined, reproducible investment screening process. Every decision is scored, weighted, and logged to JSON for audit.

Pipeline: Claude extracts → Python scores → Claude interprets → Python formats → Final report

STEP 1 — GATHER COMPANY INFORMATION

Ask the user for (or extract from their message): - Company name and sector - Stage (Pre-Seed / Seed / Series A / etc.) - Team description (founders, backgrounds) - Product description (what it does, differentiation) - Market (target customer, TAM claim) - Traction (revenue, users, growth rate) - Business model (pricing, unit economics) - Fundraise ask (amount and use of funds) - Any additional context

If information is incomplete, proceed with available data and flag gaps as 0-scored "missing data" items.

STEP 2 — CLAUDE: EXTRACT AND SCORE DIMENSIONS

Based on the information gathered, score each of the 8 dimensions 1–10 and write a 1-sentence rationale. Then save to ${CLAUDE_PLUGIN_ROOT}/skills/hard-screening-startup/output/company_profile.json:

{
  "company": "Company Name",
  "sector": "B2B SaaS",
  "stage": "Seed",
  "geography": "US",
  "scores": {
    "team": {"score": 0, "rationale": ""},
    "market": {"score": 0, "rationale": ""},
    "product": {"score": 0, "rationale": ""},
    "traction": {"score": 0, "rationale": ""},
    "business_model": {"score": 0, "rationale": ""},
    "competition": {"score": 0, "rationale": ""},
    "financials": {"score": 0, "rationale": ""},
    "risk_profile": {"score": 0, "rationale": ""}
  },
  "investment_thesis": "",
  "why_now": "",
  "key_risks": ["", "", ""],
  "dd_priorities": ["", "", ""],
  "comparables": ["", ""]
}

Scoring rubric:

Dimension Weight Key question
Team 0.25 Why is this team uniquely positioned to win?
Market 0.20 Is TAM > $1B? Growing? Right timing?
Product 0.15 What is the defensible moat?
Traction 0.15 What evidence exists that the market wants this?
Business Model 0.10 LTV:CAC > 3x? Margins > 60% for SaaS?
Competition 0.08 Why does this win vs funded incumbents?
Financials 0.05 Is burn rate reasonable? 18+ months runway?
Risk Profile 0.02 What's the realistic failure mode?

STEP 3 — PYTHON: COMPUTE WEIGHTED SCORE AND VERDICT

Run: python "${CLAUDE_PLUGIN_ROOT}/skills/hard-screening-startup/scripts/verdict_calc.py"

This script reads company_profile.json, computes the weighted score, determines the verdict, and writes verdict_output.json.

STEP 4 — CLAUDE: INTERPRET SCORES

Read verdict_output.json. Interpret the results: - If CONDITIONAL PASS: state exactly what conditions must be met - If DECLINE: be specific about which dimensions caused the decline - Expand the investment thesis into 3 full sentences - Write the full WHY NOW narrative - Elaborate on all 3 key risks with specific scenarios

STEP 5 — PYTHON: FORMAT FINAL REPORT

Run: python "${CLAUDE_PLUGIN_ROOT}/skills/hard-screening-startup/scripts/report_formatter.py"

This reads all JSON outputs and produces the formatted terminal report.

ERROR HANDLING