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
Best use case
creating-financial-models is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Teams using creating-financial-models should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/creating-financial-models/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How creating-financial-models Compares
| Feature / Agent | creating-financial-models | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
Related Skills
explaining-machine-learning-models
Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
evaluating-machine-learning-models
Build this skill allows AI assistant to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
deploying-machine-learning-models
Deploy this skill enables AI assistant to deploy machine learning models to production environments. it automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. use this skill when th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'.
optimizing-deep-learning-models
This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".
creating-data-visualizations
This skill enables Claude to generate data visualizations, plots, charts, and graphs from provided data. It analyzes the data, selects the most appropriate visualization type, and creates a visually appealing and informative graphic. Use this skill when the user requests a visualization, plot, chart, or graph; when data needs to be presented visually; or when exploring data patterns. The skill is triggered by requests for "visualization", "plot", "chart", or "graph".
creating-webhook-handlers
Create webhook endpoints with signature verification, retry logic, and payload validation. Use when receiving and processing webhook events. Trigger with phrases like "create webhook", "handle webhook events", or "setup webhook handler".
creating-kubernetes-deployments
Deploy applications to Kubernetes with production-ready manifests. Supports Deployments, Services, Ingress, HPA, ConfigMaps, Secrets, StatefulSets, and NetworkPolicies. Includes health checks, resource limits, auto-scaling, and TLS termination. Use when working with creating kubernetes deployments. Trigger with 'creating', 'kubernetes', 'deployments'.
creating-apm-dashboards
Execute this skill enables AI assistant to create application performance monitoring (apm) dashboards. it is triggered when the user requests the creation of a new apm dashboard, monitoring dashboard, or a dashboard for application performance. the skill helps ... Use when generating or creating new content. Trigger with phrases like 'generate', 'create', or 'scaffold'.
creating-ansible-playbooks
Execute use when you need to work with Ansible automation. This skill provides Ansible playbook creation with comprehensive guidance and automation. Trigger with phrases like "create Ansible playbook", "automate with Ansible", or "configure with Ansible".
creating-alerting-rules
Execute this skill enables AI assistant to create intelligent alerting rules for proactive performance monitoring. it is triggered when the user requests to "create alerts", "define monitoring rules", or "set up alerting". the skill helps define thresholds, rou... Use when generating or creating new content. Trigger with phrases like 'generate', 'create', or 'scaffold'.
building-classification-models
This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
adapting-transfer-learning-models
Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.