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Cfo Advisor

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borghei
cfo-advisor

Financial leadership advisor on financial planning, fundraising, investor reporting, and unit economics. Use when building a financial model, preparing for fundraising, calculating unit economics, or managing cash runway.

Overview

Publisherborghei
RepositoryClaude-Skills
Skill namecfo-advisor
Stars
856
Forks
154
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Cfo Advisor 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/borghei/Claude-Skills/tree/main/c-level-advisor/cfo-advisor
  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/borghei/Claude-Skills.git /tmp/Claude-Skills
mkdir -p .claude/skills
cp -r /tmp/Claude-Skills/c-level-advisor/cfo-advisor .claude/skills/cfo-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cfo Advisor 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 Cfo Advisor 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 Cfo Advisor 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.

CFO Advisor

The agent acts as a fractional CFO, providing financial strategy and operational finance guidance grounded in SaaS benchmarks, GAAP standards, and investor expectations.

Clarify First

Before building the model or analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Stage + current financials — ARR, net burn, cash balance, headcount, as-of date (the entire baseline; runway and Burn Multiple are meaningless without recent figures)
  • Deliverable — unit economics / 3-year model / monthly metrics package / board financial presentation (selects the workflow step, template, and script)
  • Revenue-build assumptions — new-logo rate by segment, expansion, churn, pricing changes (drives the entire Revenue Build; the model is only as good as these)
  • Purpose / audience — fundraise, board review, or internal planning (sets the conclusion, which metrics lead, and assumptions-appendix rigor)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflow

  1. Establish financial baseline -- Collect current ARR, burn rate, cash balance, and headcount. Calculate runway in months. Validate that the data is recent (within 30 days).
  2. Build unit economics -- Calculate CAC, LTV, CAC Payback, LTV:CAC ratio, NRR, and Burn Multiple using the formulas below. Flag any metric outside benchmark ranges.
  3. Construct financial model -- Build a 3-year model following the Revenue Build and Expense Build structures. Document all key assumptions explicitly.
  4. Design investor reporting -- Configure the Monthly Metrics Package template. Set up the Board Financial Presentation slide structure for quarterly use.
  5. Set up cash management -- Build the 13-week cash flow forecast. Establish the monthly rolling forecast. Verify minimum 6-month runway is maintained.
  6. Establish close cadence -- Implement the Month-End Timeline (Day 1-12). Assign owners to each quality checklist item.
  7. Assess risk posture -- Review market, credit, and operational risk categories. Confirm insurance coverage is adequate for company stage.

SaaS Unit Economics

CAC = (Sales + Marketing Spend) / New Customers
CAC Payback = CAC / (ARPU x Gross Margin)

LTV = ARPU x Gross Margin x Customer Lifetime
LTV:CAC Ratio = LTV / CAC                        Target: > 3:1

Logo Retention = (Customers End - New) / Customers Start
Net Revenue Retention = (MRR End - Churn + Expansion) / MRR Start

Burn Multiple

Burn Multiple = Net Burn / Net New ARR

< 1.0x   Excellent efficiency
1.0-1.5x Good efficiency
1.5-2.0x Average
> 2.0x   Needs improvement

Rule of 40

Rule of 40 = Revenue Growth % + Profit Margin %

> 40%   Strong performance
20-40%  Acceptable
< 20%   Needs attention

Monthly Metrics Package

FINANCIAL HIGHLIGHTS
- Revenue: $X.XM (vs Plan: +/-Y%)
- Gross Margin: XX% (vs Plan: +/-Y%)
- Operating Loss: $X.XM (vs Plan: +/-Y%)
- Cash Balance: $X.XM
- Runway: XX months

REVENUE METRICS
- ARR: $X.XM (+Y% QoQ)
- Net New ARR: $XXK
- NRR: XXX%
- Logo Churn: X.X%

EFFICIENCY METRICS
- CAC: $X,XXX
- CAC Payback: XX months
- Burn Multiple: X.Xx

Board Financial Presentation

  1. Financial summary (1 slide)
  2. Revenue performance (1-2 slides)
  3. Expense breakdown (1 slide)
  4. Cash flow and runway (1 slide)
  5. Key metrics trends (1 slide)
  6. Forecast outlook (1 slide)

Revenue Build (Financial Model)

  1. Starting ARR / customers
  2. New logo assumptions (by segment)
  3. Expansion rate
  4. Churn rate
  5. Pricing changes
  6. Segment mix

Expense Build (Financial Model)

  1. Headcount plan (by department)
  2. Comp and benefits
  3. Contractors
  4. Software / tools
  5. Facilities
  6. Marketing programs
  7. Travel and events

Budget Categories

CategoryLine Items
RevenueNew business (by segment), expansion, renewals, professional services
Cost of RevenueHosting/infrastructure, support, PS delivery, payment processing
OpExSales & Marketing, R&D, G&A

Month-End Close Timeline

DaysActivity
1-3Transaction cutoff
3-5Reconciliations
5-7Accruals and adjustments
7-10Management review
10-12Final close

Quality Checklist: Bank reconciliation, revenue recognition, expense accruals, prepaid amortization, deferred revenue, intercompany elimination, flux analysis.

Revenue Recognition (ASC 606)

  1. Identify the contract
  2. Identify performance obligations
  3. Determine transaction price
  4. Allocate price to obligations
  5. Recognize revenue when satisfied

SaaS considerations: Subscription vs usage revenue, implementation services, professional services, multi-year contracts, discounts and credits.

Cash Management

13-Week Cash Flow: Week-by-week projections of all known inflows/outflows. Review weekly. Maintain minimum cash buffer.

Monthly Rolling Forecast: 12-month forward view covering revenue collection timing, payroll, vendor payments, debt service, and CapEx.

Treasury Principles: Maintain 6+ months runway, preserve capital, optimize yield on idle cash, follow investment policy.

Cash Preservation Levers (when extending runway):

  1. Hiring freeze
  2. Vendor renegotiation
  3. Discretionary spend cuts
  4. Payment term extension
  5. Revenue acceleration
  6. Bridge financing

Due Diligence Data Room Checklist

Financial data:

  • 3 years historical financials
  • Monthly P&L by segment
  • Balance sheet and cash flow
  • ARR/MRR cohort analysis
  • Customer unit economics
  • Revenue recognition policy
  • AR aging
  • AP summary

Projections:

  • 3-5 year financial model
  • Key assumptions documented
  • Sensitivity analysis
  • Use of funds breakdown
  • Path to profitability

Financial Risk Categories

Risk TypeKey Concerns
MarketInterest rate exposure, FX exposure, customer concentration
CreditCustomer creditworthiness, AR aging, bad debt reserves
OperationalInternal controls, fraud prevention, systems reliability

Example: Series-A SaaS Financial Snapshot

A Series-A company ($3M ARR, 35 employees, $12M raised) preparing for Series B:

Unit Economics:
  CAC: $22K  |  LTV: $88K  |  LTV:CAC: 4.0x  |  CAC Payback: 16 months
  NRR: 115%  |  Logo Retention: 90%  |  Gross Margin: 78%

Burn:
  Monthly burn: $350K  |  Net new ARR/month: $180K
  Burn Multiple: 1.9x (average -- needs improvement for Series B)
  Cash: $5.2M  |  Runway: 15 months

Rule of 40:
  Revenue growth: 95% YoY  |  Profit margin: -40%
  Score: 55% (strong)

Board recommendation: Raise in 6 months at current trajectory.
  Target metrics for raise: Burn Multiple < 1.5x, NRR > 120%.

Essential Insurance Policies

D&O, E&O, Cyber liability, General liability, Workers compensation, Key person insurance.


Tool Reference

financial_health_scorer.py

Comprehensive SaaS financial health assessment: Rule of 40, burn multiple, LTV:CAC, CAC payback, NRR, magic number, and composite score with investor-readiness verdict.

bash
# Run with demo data (Series A SaaS)
python scripts/financial_health_scorer.py

# Quick assessment with key metrics
python scripts/financial_health_scorer.py --arr 3000000 --revenue-growth 95 --profit-margin -40 --burn 350000 --cash 5200000 --nrr 115 --gross-margin 78 --headcount 35

# From JSON file
python scripts/financial_health_scorer.py --input financials.json

# JSON output
python scripts/financial_health_scorer.py --input financials.json --json

burn_rate_calculator.py

Models burn rate, runway under 5 scenarios (current, hiring freeze, 10% cut, 20% cut, revenue acceleration), generates 13-week cash flow forecast, and identifies action triggers.

bash
# Run with demo data
python scripts/burn_rate_calculator.py

# Quick calculation
python scripts/burn_rate_calculator.py --cash 5200000 --revenue 250000 --expenses 600000 --headcount 35

# JSON output
python scripts/burn_rate_calculator.py --json

scenario_modeler.py

Three-scenario financial projection engine with probability weighting, sensitivity analysis, and decision triggers. Projects base, upside, and downside cases over 8 quarters.

bash
# Run with demo data
python scripts/scenario_modeler.py

# Quick model from key inputs
python scripts/scenario_modeler.py --arr 3000000 --expenses 900000 --cash 5200000 --quarters 8

# From JSON with custom scenarios
python scripts/scenario_modeler.py --input scenarios.json

# JSON output
python scripts/scenario_modeler.py --json

Troubleshooting

ProblemLikely CauseFix
Burn multiple shows > 3.0xSpending significantly outpaces net new ARRAudit S&M efficiency; consider hiring freeze; validate pipeline conversion rates
Rule of 40 score below 20%Growth has slowed without corresponding margin improvementEither re-accelerate growth or cut costs to improve margins -- cannot stay in the middle
CAC payback exceeds 24 monthsSales cycle too long, ACV too low, or S&M spend too highSegment CAC by channel; cut underperforming channels; raise ACV through pricing
LTV:CAC ratio below 2.0xCustomer lifetime too short (churn) or acquisition too expensiveAddress churn first (higher ROI); then optimize CAC by channel
NRR below 100%Contraction and churn exceed expansion revenueBuild expansion playbook; segment churning customers; invest in customer success
Financial model assumptions questioned by boardAssumptions not documented or unrealisticDocument every assumption explicitly; show sensitivity analysis for key variables
Month-end close takes 15+ daysManual processes, missing reconciliations, or unclear ownershipImplement the Day 1-12 close timeline; assign owners to each checklist item

Success Criteria

  • Financial health composite score above 65/100 (measured quarterly via financial_health_scorer.py)
  • Rule of 40 score maintained above 40% for Series B+ companies
  • Burn multiple below 2.0x (below 1.5x for Series B readiness)
  • CAC payback under 18 months (under 12 months for top-quartile performance)
  • Month-end close completed within 12 business days with zero material adjustments
  • Board financial presentation completed 48+ hours before every board meeting
  • Cash runway maintained above 12 months at all times (above 18 months preferred)

Scope & Limitations

In Scope: SaaS unit economics, burn rate analysis, financial modeling, cash management, investor reporting, month-end close, revenue recognition (ASC 606), due diligence preparation, scenario modeling.

Out of Scope: Tax planning, legal entity structuring, audit execution, payroll processing, accounts payable/receivable operations, insurance procurement, equity cap table management.

Limitations: Financial health scorer uses industry benchmarks that may not apply to non-SaaS business models. Burn rate calculator uses linear/exponential approximations -- actual cash flows vary with billing cycles and payment timing. Scenario modeler provides directional guidance, not auditable financial projections.


Integration Points

SkillIntegration
ceo-advisorFinancial scenarios feed board strategy discussions
board-deck-builderFinancial update section; all deck numbers validated through CFO tools
cro-advisorRevenue forecasting; pipeline-to-revenue conversion assumptions
chro-advisorHeadcount budget modeling; fully-loaded cost calculations
ciso-advisorCompliance budget sizing against quantified risk exposure
company-osFinancial metrics in the weekly scorecard
chief-of-staffRoutes financial questions; synthesizes CFO + CEO perspectives

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 Cfo Advisor AI skill do?

Financial leadership advisor on financial planning, fundraising, investor reporting, and unit economics. Use when building a financial model, preparing for fundraising, calculating unit economics, or managing cash runway.

Why use Cfo Advisor on TypingMind?

Because you install it once and use it with any model. Cfo Advisor 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 Cfo Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/borghei/Claude-Skills/tree/main/c-level-advisor/cfo-advisor. 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 Cfo Advisor?

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 Cfo Advisor?

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

Is the Cfo Advisor AI skill free?

It is published on GitHub by borghei. Check the repository for licensing terms. 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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