Financial Modeling logo

Financial Modeling

Organization
LeadMagic
financial-modeling

SaaS financial modeling for founders — operating model, runway, headcount, DCF valuation, consumption-based pricing models, cohort analysis, unit economics by segment, ARR bridge/build, 3-scenario forecasting, and board-ready financials. Use when building financial model, projecting runway, modeling fundraising, valuation, or planning headcount. Covers discounted cash flow, consumption revenue models, expansion modeling, and SaaS-specific valuation.

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill namefinancial-modeling
Stars
72
Forks
18
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeadMagic on GitHub. Read the source before you install it.

Installation

Install the Financial Modeling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeadMagic/gtm-skills/tree/main/skills/founder-led/financial-modeling
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/founder-led/financial-modeling .claude/skills/financial-modeling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Financial Modeling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Financial Modeling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Financial Modeling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SaaS Financial Modeling

Overview

Most founder financial models are fiction — aggressive growth curves with no operational logic underneath. The mistake: building a top-down model ("we'll capture 1% of a $100B market") instead of a bottom-up operating model ("we'll hire 3 AEs at $120K OTE, each producing $800K quota in month 6"). This skill covers building a real operating model that investors trust, boards respect, and YOU can use to make decisions. Now includes DCF valuation, consumption-based pricing models, cohort analysis, and full ARR bridge/build methodology.

When to Use

Trigger phrases: "financial model", "SaaS P&L", "runway calculation", "DCF valuation", "consumption pricing model", "cohort analysis", "ARR bridge", "headcount plan", "revenue forecast", "409A valuation", "unit economics model", "annual GTM budget", "operating budget", "variance review", "QuickBooks vs NetSuite"

Authoritative Foundations

David Skok — SaaS Unit Economics

LTV = (ARPA × Gross Margin %) ÷ Monthly Churn Rate. LTV/CAC > 3x is investable. CAC Payback < 12 months is good.

Aswath Damodaran — DCF Valuation

"The value of any asset is the present value of its expected future cash flows." For startups: high growth + high risk = high WACC (20-30% for early stage).

Ben Murray — The SaaS CFO

Three P&L layers: Revenue (new + expansion - churn), COGS (20-30%), OpEx (S&M + R&D + G&A). Model per-head where possible. PE exits require EBITDA bridge — see references/unit-economics-exit-bridge.md.

Meritech Capital — Public SaaS Benchmarks

For IPO-track scenarios, compare implied ARR growth, FCF margin, and Meritech Rule of 40 to public medians. references/meritech-saas-benchmarks.md. Private stage gates remain in references/benchmark-reconciliation.md.

Force Management — Pod Economics & Headcount Planning

Bottom-up headcount from pod economics: group SDR/AE/SE/CSM into pods, run pod cost % of ARR (target ≤35% at 80% ramp), link quota attainment distribution to hiring triggers. See references/force-management-playbook.md and references/unit-economics-exit-bridge.md.

Step-by-Step Process

Phase 1: Revenue Model (Bottom-Up)

Do NOT model "1% of market." Model customers × average revenue.

Monthly Revenue Model:

New Customers = (SDR meetings × demo conversion rate) + inbound + expansion
Average ACV = $X (new logo) / $X (expansion)
Monthly Churn = Previous month customers × monthly churn rate
Net New MRR = (New × ACV) + Expansion - Churned

Revenue inputs to get right:

  • SDR productivity: 8-12 meetings/month per SDR
  • Demo conversion: 20-30% for qualified meetings
  • Sales cycle: 30-90 days depending on ACV
  • AE ramp: 3-6 months to full quota
  • Monthly churn: 1-3% SMB, 0.5-1% mid-market, <0.5% enterprise

Phase 2: Headcount Model

Model every hire with: role, salary, start month, ramp, fully-loaded cost. Fully-loaded = base × 1.25-1.35 (benefits, payroll tax, tools, equipment).

S&M Headcount by ARR Stage (benchmarks):

ARRAEsSDRsCSMsManagers
$0-1MFounder(s)———
$1-3M1-32-41—
$3-10M5-105-103-5SDR Mgr, VP Sales
$10-20M10-2010-155-10Full management layer

Phase 3: Operating P&L

MONTHLY P&L STRUCTURE:

REVENUE: New MRR + Expansion MRR - Churned MRR = Net New MRR
COST OF REVENUE (20-30%): Hosting, APIs, support team
GROSS PROFIT / Gross Margin %
OPERATING EXPENSES:
  S&M: Headcount + tools + ad spend + events
  R&D: Headcount + tools + infra
  G&A: Headcount + rent + legal + SaaS tools
NET OPERATING INCOME (Loss)

Phase 4: Cash Flow & Runway

Starting Cash: $X
+ Inflows: customer cash (monthly/annual) + expansion
- Outflows: payroll (60-70%), infra, tools, marketing, rent, legal
= Net Cash Burn
Runway (months) = Cash ÷ |Monthly Net Cash Burn|

Runway rules: 18+ months post-raise. Start fundraising at 9-12 months left. Cash-zero date is NOT when you start fundraising — it's when you're dead.

Phase 5: Scenario Planning

Load references/gtm-budget-playbook.md for annual S&M/R&D/G&A structure, headcount-driven build, vendor budget tie to gtm-spend-management, and monthly variance cadence. Worksheet → the gtm-spend-management skill (annual-gtm-budget-worksheet.md).

DriverConservativeBase CaseAggressive
Monthly churn3%2%1%
Demo conversion15%25%35%
ACV$10K$15K$20K
AE ramp (months)643
Hiring speed45 days30 days21 days

Conservative should feel uncomfortable. It's a stress test, not a wish.

Consumption-Based Pricing Models

What they are: Customers pay based on usage, not seats. Examples: AWS (per compute hour), Twilio (per SMS/API call), Snowflake (per credit), Stripe (per transaction). Consumption models align price with value.

Modeling consumption revenue:

CONSUMPTION REVENUE MODEL

Base Metric: [API calls / compute hours / events / contacts]
Usage Tiers:
- Light (0-1K units): X% of customers, avg Y units/mo, $Z/unit
- Medium (1K-10K): X% of customers, avg Y/mo, $Z/unit
- Heavy (10K-100K): X% of customers, avg Y/mo, $Z/unit
- Enterprise (100K+): X% of customers, avg Y/mo, $Z/unit

Monthly Revenue = Σ (customers in tier × avg usage × unit price)

Key consumption metrics:

  • NRR: Best-in-class consumption companies hit 130%+ NRR (usage compounds)
  • Usage Growth Rate: 5-10% MoM per existing customer = healthy
  • Dormancy Rate: % of customers with zero usage in a month. <5% healthy.
  • Commitment Coverage: % of committed spend consumed. <80% at renewal = risk.

Consumption model pitfalls:

  • Revenue unpredictability → Fix: Annual commitments with true-up
  • Pricing complexity → Fix: Usage dashboards, spend alerts, never-exceed caps
  • Volume discounting → Fix: Tiered pricing from day 1, no custom discounts

Hybrid models (best of both):

  • Platform fee (predictable) + consumption (growth-aligned)
  • Seat minimum + usage above threshold
  • "Starter includes X units, then $Y/unit above"

Expansion Revenue Modeling

The 5 expansion levers:

LeverModel AsContribution to NRR
Seat expansionNew seats × ACV3-8% of starting ARR
Usage growthConsumption increase × unit price5-15% of starting ARR
Tier upgrade(New price - Old) × Upgrading2-5% of starting ARR
Cross-sellNew product ACV × Attach rate2-10% of starting ARR
Price increasesIncrease % × Retained customers3-5% of starting ARR

Expansion formula:

Expansion ARR = (Starting ARR × Seat Exp %) +
                (Starting Usage × Usage Growth % × Unit Price) +
                (Upgrading × Tier Delta) +
                (Eligible × Cross-Sell Attach % × Cross-Sell ACV) +
                (Retained × Price Increase %)

DCF Valuation for SaaS Companies

When to use DCF: Fundraising, 409A valuations, acquisition offers, or understanding what your company is actually worth. DCF is the gold standard.

The DCF formula:

Enterprise Value = Σ (FCF_t ÷ (1 + WACC)^t) + Terminal Value

Where:
- FCF = EBIT × (1 - Tax Rate) + D&A - Capex - Δ Working Capital
- WACC = 12-25% for startups (higher = riskier stage)
- t = year 1 through 5
- Terminal Value = FCF_Year5 × (1 + g) ÷ (WACC - g)
  (g = perpetual growth rate, 2-4%)

DCF Step-by-Step

Step 1: Project Revenue (5 years)

Year 1: Starting ARR × (1 + growth rate)
Year 2: Year 1 × (1 + decaying growth rate)
...decay toward terminal growth rate (3-5%)

Growth rate decay (benchmarks):

YearEarly StageGrowth StageMature
1150-300%60-100%30-50%
280-150%40-80%25-40%
350-80%30-50%20-30%
430-50%20-35%15-25%
520-35%15-25%10-20%

Step 2: Project Margins

Gross Margin: 65-75% early → 75-85% mature
S&M as % of Revenue: 80-120% early → 20-30% mature
R&D: 40-60% early → 15-25% mature
G&A: 15-25% early → 8-12% mature
EBITDA Margin: -50% early → 15-30% mature

Step 3: Calculate Free Cash Flow

Revenue - COGS = Gross Profit
- S&M - R&D - G&A = EBITDA
- D&A (2-5% of revenue) = EBIT
- Taxes (21% federal + state, startup NOLs typically offset)
= NOPAT
+ D&A - Capex (3-5% revenue) - Δ Working Capital (5-10% revenue change)
= Unlevered Free Cash Flow

Step 4: Calculate WACC

Cost of Equity = Risk-Free Rate (~4%) + (Beta × ERP) + Startup Premium
- Beta: 1.5-2.5 for SaaS
- ERP (Equity Risk Premium): 5-6%
- Startup Premium: 5-15% depending on stage

Early stage WACC: 20-30%
Growth stage WACC: 15-20%
Mature SaaS WACC: 10-15%

Step 5: Terminal Value

TV = FCF_Year5 × (1 + g) ÷ (WACC - g)
g = 2-4% (can't exceed GDP growth long-term)

Step 6: Enterprise Value & Sensitivity

EV = PV(Year 1-5 FCFs) + PV(Terminal Value)

SENSITIVITY TABLE (WACC × Terminal Growth):
| WACC →    | 12%   | 15%   | 18%   | 22%   | 25%   |
| g = 2%    | $X M  | $X M  | $X M  | $X M  | $X M  |
| g = 3%    | $X M  | $X M  | $X M  | $X M  | $X M  |
| g = 4%    | $X M  | $X M  | $X M  | $X M  | $X M  |

DCF quick-check (reality test):

  • Early-stage SaaS: 10-20× forward ARR (growth rate dependent)
  • Growth-stage: 8-15× forward ARR
  • Mature: 5-10× forward ARR If your DCF is outside these bands, check your assumptions.

For exit planning, connect unit economics → P&L → ARR and EBITDA multiples via references/unit-economics-exit-bridge.md. Sensitivity template → exiting-company/templates/valuation-sensitivity-table.md.

Earn-out / deferred consideration: Model cash at close, earn-out scenarios (base / max), and 3-year hold — not headline EV alone. Worksheet → exiting-company/templates/earn-out-term-sheet-review.md. Structure norms → references/benchmark-reconciliation.md. Typical not guaranteed — CPA for tax timing.

DCF Pitfalls for Startups

  1. Terminal value dominates. 80-90% of startup DCF = terminal value (most speculative part). Fix: Conservative terminal growth (2-3%). Wide sensitivity.
  2. Growth rate optimism. "100% growth for 5 years" = nonsense. Fix: Model realistic decay. Even the best decay toward market growth rates.
  3. WACC too low. 10% WACC for seed stage = utility-level risk pricing. Fix: Early stage WACC = 20-30%.
  4. No sensitivity. Single-point DCF = guess. Range = analysis. Fix: Always build sensitivity table.

SaaS Cohort & Unit Economics Deep-Dive

Cohort Analysis

Why cohorts matter: Aggregate churn of 2% hides that Q1 cohort churns at 5% and Q4 at 0.5%. One is a problem you can't see in aggregate.

Cohort retention curve (power law): Retention(t) = a × t^(-b). Where a ≈ 100%, b = churn decay factor.

Monthly cohort table:

CohortM0 MRRM1M3M6M12Est LTV
Jan 2025$X$X$X$X$X$X
Feb 2025$X$X$X$X—$X (est)

Cohort CAC payback: Aggregate "8 months" can hide Q2 cohort taking 14 months. Fix: Track CAC payback per cohort, not just aggregate.

Unit Economics by Segment

SegmentACVChurnLTVCACLTV:CACPayback
SMB (<$1K ACV)$XX%$X$XXxX mo
Mid-Market ($1-10K)$XX%$X$XXxX mo
Enterprise ($10K+)$XX%$X$XXxX mo

Decision rule: If LTV:CAC < 3x, stop acquiring in that segment until fixed.

ARR Build vs. ARR Bridge

ARR Build (forward-looking): Starting ARR + New ARR + Expansion - Churned - Contraction = Ending ARR

ARR Bridge (backward analysis): Starting ARR + New Logo + Expansion - Logo Churn - Contraction - Downsell = Ending

The bridge tells WHERE growth came from. The build projects WHERE it will come from.

Revenue lens: Model recognized revenue in P&L; use committed MRR bridge for board ARR/NRR. Reconcile gaps (prepay, PS, implementation delay) via references/saas-mrr-accounting-nuances.md and references/bookings-billings-revenue-matrix.md.

Accounting Stack for SaaS (Founder Timing)

StageStackHire trigger
Pre-$500K ARRQuickBooks Online or Xero + Stripe/billing + bookkeeper (Pilot/Bench class)Founder + part-time bookkeeper
$500K–$3M ARRQBO/Xero + billing + fractional CFOFirst finance hire = controller or strong fractional CFO
$3M–$15M ARREvaluate NetSuite; dedicated controllerVP Finance when board/investor reporting >20 hrs/mo founder
$15M+ / multi-entityNetSuite or ERPFull-time CFO

Chart of accounts (minimum): Subscription revenue, PS revenue, COGS (hosting), S&M, R&D, G&A, deferred revenue.

Tax / 409A / QSBS awareness (not advice): references/saas-tax-founder-awareness.md · Carta 409A · CPA handoff triggers.

Bootstrap path: solo-founder-gtm · saas-outcomes/templates/bootstrap-capital-plan.md.

Output Format

SAAS FINANCIAL MODEL — [Company]

Summary (Base Case — 36 months):
- Ending ARR: $X M
- Headcount: X
- Gross Margin: X%
- Magic Number: X
- LTV:CAC: Xx
- CAC Payback: X months
- NRR: X%
- Rule of 40: X%
- Runway: X months
- Break-Even: [month/year or N/A]

DCF Valuation:
- Enterprise Value (Base): $X M
- Sensitivity Range: $X M - $Y M
- Implied ARR Multiple: Xx

Consumption Model (if applicable):
- Unit: [metric], Unit Price: $X
- Usage Growth Rate: X% MoM
- Dormancy Rate: X%

Key Assumptions: [10-15 key drivers documented]
Funding Requirement: $X M at month Y, runway post-raise: X months

Implementation Checklist

  • Revenue modeled bottom-up (customers × ACV), not top-down (market %)
  • Headcount has every role, start date, and fully-loaded cost (1.3x salary)
  • Churn modeled monthly with compounding, not annual averaging
  • AE ramp curve built (0% M1, 50% M3, 100% M6)
  • Three scenarios: Conservative, Base, Aggressive (internally consistent)
  • Cash flow includes ALL costs (insurance, legal, SaaS tools)
  • LTV/CAC uses fully-loaded CAC (all S&M, not just ad spend)
  • DCF uses appropriate WACC (20-30% early stage)
  • Terminal growth capped at 3-4% (GDP growth ceiling)
  • Sensitivity table built (WACC ±5%, growth ±10%, terminal g ±1%)
  • Cohort analysis segmented (not just aggregate metrics)
  • ARR bridge built alongside ARR build (where growth came from)

Quality Check

Before delivering, verify:

  • Output matches the user's stated request
  • Named frameworks or sources are reflected in the recommendation
  • The deliverable is specific enough for an agent to execute
  • Any assumptions, risks, or dependencies are explicit
  • No unsupported claims, invented facts, or private/internal references are included

Common Pitfalls

  1. Top-down revenue modeling. "1% of $100B market" = fiction. Fix: Model customers × ACV with realistic acquisition.

  2. Zero churn assumption. No SaaS has zero churn. Fix: 1-3% monthly churn minimum for SMB, 0.5-1% for mid-market.

  3. Instant AE productivity. AEs don't close month 1. Fix: Ramp curve: 0% M1, 50% M3, 100% M6.

  4. Terminal value dominates DCF. 80-90% of startup value from terminal = speculative. Fix: Conservative terminal growth (2-3%). Wide sensitivity.

  5. WACC too low for startups. 10% WACC for seed stage implies utility-level risk. Fix: Early stage WACC = 20-30%. Risk is real.

  6. Aggregate metrics hiding segment problems. 2% churn average hides 5% SMB churn and 0.5% Enterprise. Fix: Segment everything.

Execution Artifacts

  • references/framework-notes.md — Named frameworks and reference tables
  • templates/output-template.md — Deliverable shell for agent output
  • scripts/check-output.py — Lightweight deliverable validator
  • references/gtm-budget-playbook.md — canonical annual GTM budget
  • the gtm-spend-management skill (annual-gtm-budget-worksheet.md) — budget worksheet (gtm-spend-management skill)
  • references/saas-mrr-accounting-nuances.md — MRR/recognition reconcile
  • references/saas-tax-founder-awareness.md — tax/409A/QSBS handoffs
  • references/bookings-billings-revenue-matrix.md — cash vs committed ARR
  • references/unit-economics-exit-bridge.md — LTV/NRR/EBITDA → valuation multiple bridge Cross-skill (journey / exit): gtm-spend-management (vendor budget) · saas-metrics-calculator (MRR bridge) · saas-outcomes/references/journey-stage-gates.md, saas-outcomes/references/bootstrap-founder-playbook.md, saas-outcomes/templates/bootstrap-capital-plan.md, saas-outcomes/templates/journey-planning-worksheet.md, saas-outcomes/references/valuation-multiples.md, exiting-company/templates/valuation-sensitivity-table.md, exiting-company/templates/earn-out-term-sheet-review.md, exiting-company/references/negotiating-earn-out.md, exiting-company/references/buyer-readiness-checklist.md, saas-metrics-calculator/references/metric-definitions-exit-weight.md, references/benchmark-reconciliation.md

⚠️ Disclaimer

This skill provides general informational guidance based on publicly available frameworks and operator experience. It is NOT legal advice, accounting advice, tax advice, financial advice, insurance advice, or professional services advice.

Consult qualified professionals for your specific situation — attorneys for legal/equity matters, CPAs for tax and accounting, licensed brokers for insurance, and certified security assessors for compliance. This skill does not create a professional-client relationship. Use it as a starting point for research and preparation.

Related Skills

  • saas-metrics-calculator — Complete SaaS metrics with stage benchmarks
  • fundraising-strategy — Fundraising process, SAFEs, term sheets
  • board-meeting-prep — Board deck, metrics presentation
  • pricing-strategy — Tier design, monetization models
  • pricing-psychology — Anchoring, decoy effect, willingness-to-pay
  • investor-updates — Monthly investor communications
  • co-founder-dynamics — Equity modeling, co-founder equity splits
  • yc-ecosystem — YC funding context and valuation benchmarks

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Financial Modeling AI skill do?

SaaS financial modeling for founders — operating model, runway, headcount, DCF valuation, consumption-based pricing models, cohort analysis, unit economics by segment, ARR bridge/build, 3-scenario forecasting, and board-ready financials. Use when building financial model, projecting runway, modeling fundraising, valuation, or planning headcount. Covers discounted cash flow, consumption revenue models, expansion modeling, and SaaS-specific valuation.

Why use Financial Modeling on TypingMind?

Because you install it once and use it with any model. Financial Modeling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Financial Modeling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/founder-led/financial-modeling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Financial Modeling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Financial Modeling?

As many as you like. As long as a model supports skills, you can use Financial Modeling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Financial Modeling AI skill free?

Yes. It is published on GitHub by LeadMagic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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