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Creating Financial Models

Community
ynulihao
creating-financial-models

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

Overview

Publisherynulihao
RepositoryAgentSkillOS
Skill namecreating-financial-models
Stars
612
Forks
76
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Creating Financial Models 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.
  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/ynulihao/AgentSkillOS.git /tmp/AgentSkillOS
mkdir -p .claude/skills
cp -r /tmp/AgentSkillOS/data/skill_seeds/creating-financial-models .claude/skills/creating-financial-models
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Creating Financial Models 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 Creating Financial Models 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 Creating Financial Models 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.

Financial Modeling Suite

A comprehensive financial modeling toolkit for investment analysis, valuation, and risk assessment using industry-standard methodologies.

Core Capabilities

1. Discounted Cash Flow (DCF) Analysis

  • Build complete DCF models with multiple growth scenarios
  • Calculate terminal values using perpetuity growth and exit multiple methods
  • Determine weighted average cost of capital (WACC)
  • Generate enterprise and equity valuations

2. Sensitivity Analysis

  • Test key assumptions impact on valuation
  • Create data tables for multiple variables
  • Generate tornado charts for sensitivity ranking
  • Identify critical value drivers

3. Monte Carlo Simulation

  • Run thousands of scenarios with probability distributions
  • Model uncertainty in key inputs
  • Generate confidence intervals for valuations
  • Calculate probability of achieving targets

4. Scenario Planning

  • Build best/base/worst case scenarios
  • Model different economic environments
  • Test strategic alternatives
  • Compare outcome probabilities

Input Requirements

For DCF Analysis

  • Historical financial statements (3-5 years)
  • Revenue growth assumptions
  • Operating margin projections
  • Capital expenditure forecasts
  • Working capital requirements
  • Terminal growth rate or exit multiple
  • Discount rate components (risk-free rate, beta, market premium)

For Sensitivity Analysis

  • Base case model
  • Variable ranges to test
  • Key metrics to track

For Monte Carlo Simulation

  • Probability distributions for uncertain variables
  • Correlation assumptions between variables
  • Number of iterations (typically 1,000-10,000)

For Scenario Planning

  • Scenario definitions and assumptions
  • Probability weights for scenarios
  • Key performance indicators to track

Output Formats

DCF Model Output

  • Complete financial projections
  • Free cash flow calculations
  • Terminal value computation
  • Enterprise and equity value summary
  • Valuation multiples implied
  • Excel workbook with full model

Sensitivity Analysis Output

  • Sensitivity tables showing value ranges
  • Tornado chart of key drivers
  • Break-even analysis
  • Charts showing relationships

Monte Carlo Output

  • Probability distribution of valuations
  • Confidence intervals (e.g., 90%, 95%)
  • Statistical summary (mean, median, std dev)
  • Risk metrics (VaR, probability of loss)

Scenario Planning Output

  • Scenario comparison table
  • Probability-weighted expected values
  • Decision tree visualization
  • Risk-return profiles

Model Types Supported

  1. Corporate Valuation

    • Mature companies with stable cash flows
    • Growth companies with J-curve projections
    • Turnaround situations

  2. Project Finance

    • Infrastructure projects
    • Real estate developments
    • Energy projects

  3. M&A Analysis

    • Acquisition valuations
    • Synergy modeling
    • Accretion/dilution analysis

  4. LBO Models

    • Leveraged buyout analysis
    • Returns analysis (IRR, MOIC)
    • Debt capacity assessment

Best Practices Applied

Modeling Standards

  • Consistent formatting and structure
  • Clear assumption documentation
  • Separation of inputs, calculations, outputs
  • Error checking and validation
  • Version control and change tracking

Valuation Principles

  • Use multiple valuation methods for triangulation
  • Apply appropriate risk adjustments
  • Consider market comparables
  • Validate against trading multiples
  • Document key assumptions clearly

Risk Management

  • Identify and quantify key risks
  • Use probability-weighted scenarios
  • Stress test extreme cases
  • Consider correlation effects
  • Provide confidence intervals

Example Usage

"Build a DCF model for this technology company using the attached financials"

"Run a Monte Carlo simulation on this acquisition model with 5,000 iterations"

"Create sensitivity analysis showing impact of growth rate and WACC on valuation"

"Develop three scenarios for this expansion project with probability weights"

Scripts Included

  • dcf_model.py: Complete DCF valuation engine
  • sensitivity_analysis.py: Sensitivity testing framework

Limitations and Disclaimers

  • Models are only as good as their assumptions
  • Past performance doesn't guarantee future results
  • Market conditions can change rapidly
  • Regulatory and tax changes may impact results
  • Professional judgment required for interpretation
  • Not a substitute for professional financial advice

Quality Checks

The model automatically performs:

  1. Balance sheet balancing checks
  2. Cash flow reconciliation
  3. Circular reference resolution
  4. Sensitivity bound checking
  5. Statistical validation of Monte Carlo results

Updates and Maintenance

  • Models use latest financial theory and practices
  • Regular updates for market parameter defaults
  • Incorporation of regulatory changes
  • Continuous improvement based on usage patterns

Frequently asked questions

What does the Creating Financial Models AI skill do?

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

Why use Creating Financial Models on TypingMind?

Because you install it once and use it with any model. Creating Financial Models 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 Creating Financial Models in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ynulihao/AgentSkillOS/tree/main/data/skill_seeds/creating-financial-models. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Creating Financial Models?

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 Creating Financial Models?

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

Is the Creating Financial Models AI skill free?

It is published on GitHub by ynulihao. 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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