Creating Financial Models

Use when user wants to build financial models, DCF analysis, valuation, sensitivity analysis, e-commerce business planning, investment decisions, or project...

Security Scan

The skill's stated capabilities (Monte Carlo, sensitivity, PDF reports, charts) exceed what the included code actually implements and it omits dependency/usage details — functionally useful but internally inconsistent.

Purpose & Capability

The declared purpose (financial modeling / DCF valuation) matches the included artifact: a DCF Python script and methodology notes. There are no unrelated credentials, binaries, or config paths requested.

Instruction Scope

SKILL.md promises a full suite (sensitivity/tornado charts, Monte Carlo simulations, Excel workbook + PDF report) and example outputs (output/model.xlsx, output/summary.pdf). The provided script (scripts/dcf_model.py) implements only a simple DCF-to-Excel exporter and prints enterprise value; it does not generate PDFs, charts, sensitivity tables, or Monte Carlo runs. The README instructs running the script but overstates what the script produces.

Install Mechanism

No install spec (instruction-only) which reduces risk, but the script depends on Python and the third-party package xlsxwriter. The skill does not declare these requirements; users may encounter missing-dependency errors. No network downloads or external installers are present.

Credentials

The skill requests no environment variables, credentials, or config paths. The code does not access the network or other system secrets. Requested access is proportional to its (limited) functionality.

Persistence & Privilege

The skill is not forced-always, does not request elevated or persistent privileges, and does not modify other skills or system-wide settings. Autonomous invocation is allowed by platform default but is not combined with other high-risk indicators here.

What to consider before installing

This skill appears to be a legitimate financial-modeling helper, but it is internally inconsistent: SKILL.md claims sensitivity analysis, Monte Carlo, charts, and PDF reports while the only included script performs a simple DCF and writes an Excel file. Before installing/using: 1) confirm you have Python and the xlsxwriter package (pip install xlsxwriter) or ask the author to declare dependencies; 2) don't expect Monte Carlo/sensitivity/PDF outputs unless additional scripts are provided or the author updates the code; 3) review the script for numeric edge cases (e.g., wacc == terminal_growth will divide by zero) and test with non-production data in a sandbox; 4) if you need the extra features, request the missing implementations or updated documentation from the maintainer. These inconsistencies look like incomplete/poor documentation rather than malicious behavior, but verify before relying on the skill for important valuations.

This skill provides a comprehensive suite for building financial models, including Discounted Cash Flow (DCF) analysis, sensitivity testing, Monte Carlo simulations, and scenario planning. It is ideal for valuation, investment decisions, e‑commerce business planning, and any situation requiring rigorous financial analysis.

Overview

• Core workflow: Gather inputs → run DCF model → perform sensitivity analysis → optional Monte Carlo simulation → scenario comparison.
• Outputs: Excel workbook with full model, valuation summary, charts, and a PDF report.

Core Workflow

1. Collect data
   • Historical financial statements (3‑5 years).
   • Revenue growth assumptions, margin forecasts, capex, working‑capital needs.
   • Discount rate components (risk‑free rate, beta, market premium) to compute WACC.
2. Run DCF model (scripts/dcf_model.py)
   • Projects free cash flow, computes terminal value, discounts to present value.
   • Generates enterprise and equity valuations.
3. Sensitivity analysis (built‑in in the script)
   • Varies key inputs (growth rate, WACC) and produces tornado charts.
4. Monte Carlo simulation (optional)
   • Executes thousands of iterations with probabilistic inputs, returns confidence intervals.
5. Scenario planning
   • Define best/base/worst cases, assign probabilities, compare outcomes.
6. Deliver output
   • output/model.xlsx – full model.
   • output/summary.pdf – executive summary with charts.

Usage Examples

• Create a DCF valuation for Acme Corp using the attached financials.
• Run a sensitivity analysis on the WACC and growth rate for the startup model.
• Perform a Monte Carlo simulation with 5,000 iterations on the acquisition model.
• Generate best, base, and worst case scenarios for the new e‑commerce platform rollout.

References

• See references/methodology.md for detailed methodology, assumptions, and best‑practice guidelines.

Scripts

• scripts/dcf_model.py – Core DCF engine (Python). Use python scripts/dcf_model.py <input.json> <output.xlsx>.

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Created by Simon Cai · More e-commerce skills: github.com/simoncai519/open-accio-skill