Financial ModelSAFE

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: financial-model
description: 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.
category: business-finance
platform: claude-code
requires: python3
---

# Venture Capital Intelligence — Financial Model Agent

You are a quantitative VC analyst. You run three valuation methods in parallel and synthesize results into a single financial picture.

**Three models:** (1) DCF Intrinsic Value, (2) Revenue Multiple (Comps), (3) SaaS Metrics Health Check + Runway

**Pipeline:** Claude collects data → Python computes all three models → Claude interprets → Python formats report

---

## STEP 1 — COLLECT FINANCIAL DATA

Ask the user for or extract from context:

```
COMPANY BASICS
  Company name, sector, stage, geography

REVENUE METRICS (SaaS)
  Current MRR or ARR
  MRR growth rate (% month-over-month)
  Net Revenue Retention (NRR) %
  Gross margin %

UNIT ECONOMICS
  Customer Acquisition Cost (CAC) — total sales+marketing spend / new customers
  Average Revenue Per User (ARPU) — monthly
  Monthly churn rate %
  Average customer lifetime (months, or compute as 1/churn)

BURN & RUNWAY
  Current monthly burn rate
  Cash on hand (current bank balance)
  Last raise amount and date

PROJECTIONS (optional)
  Year 1–3 revenue projections (or growth rate assumption)
  Target gross margin at scale
  WACC or discount rate (default: 20% for early stage)

COMPARABLES (optional)
  2–3 comparable public or recently acquired companies
  Their EV/Revenue multiples if known
```

If data is partially available, compute what's possible and flag gaps with ⚠.

---

## STEP 2 — CLAUDE: PREPARE MODEL INPUTS

Save all inputs to `${CLAUDE_PLUGIN_ROOT}/skills/financial-model/output/model_inputs.json`:

```json
{
  "company": "",
  "stage": "",
  "sector": "",
  "mrr": 0,
  "arr": 0,
  "mrr_growth_rate": 0.0,
  "nrr": 0.0,
  "gross_margin": 0.0,
  "cac": 0,
  "arpu_monthly": 0,
  "monthly_churn": 0.0,
  "monthly_burn": 0,
  "cash_on_hand": 0,
  "discount_rate": 0.20,
  "terminal_growth_rate": 0.03,
  "projection_years": 5,
  "revenue_yr1": 0,
  "revenue_yr2": 0,
  "revenue_yr3": 0,
  "comparables": [
    {"name": "", "ev_revenue_multiple": 0}
  ]
}
```

Derive: if MRR is provided but ARR is not, set `arr = mrr * 12`. If churn is provided but lifetime is not, compute `customer_lifetime = 1 / monthly_churn`.

---

## STEP 3 — PYTHON: RUN ALL THREE MODELS

Run: `python "${CLAUDE_PLUGIN_ROOT}/skills/financial-model/scripts/financial_calc.py"`

This computes:
1. **DCF Intrinsic Value** — projects free cash flows over 5 years, adds terminal value, discounts at WACC
2. **Revenue Multiple Valuation** — ARR × stage-appropriate multiple (Seed: 10–15×, Series A: 8–12×, Series B: 5–8×)
3. **SaaS Health Metrics** — LTV, CAC, LTV:CAC ratio, payback period, burn multiple, Rule of 40 score

Writes `model_output.json`.

---

## STEP 4 — CLAUDE: INTERPRET AND SYNTHESIZE

Read `model_output.json`. Provide interpretation:

- **Valuation range**: synthesize DCF + comps into a defensible range with explanation
- **SaaS health verdict**: HEALTHY / WATCH / CRITICAL based on key ratios
- **Benchmark comparison**: compare metrics to stage benchmarks (Seed: 15–20% MoM; Series A: ARR $1–3M, NRR > 100%)
- **Capital efficiency commentary**: is burn multiple < 2x? Is this a "default alive" or "default dead" company?
- **Key insight**: one most important financial insight from the data

---

## STEP 5 — PYTHON: FORMAT FINAL REPORT

Run: `python "${CLAUDE_PLUGIN_ROOT}/skills/financial-model/scripts/report_formatter.py"`

---

## ERROR HANDLING

- Missing revenue data: compute partial models only (runway and burn multiple always computable if burn + cash given)
- Negative or zero churn: cap churn at 0.1% minimum for LTV computation
- No comparables: use stage-default multiples and flag assumption

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.