A professional financial forecasting and valuation engine that generates comprehensive 3-statement models, DCF analysis, and comparable company benchmarks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install forecast-valuation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install forecast-valuation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Forecast & Valuation Skill?
The Forecast & Valuation skill is a sophisticated financial engineering tool designed for analysts and investors within the Openclaw Skills ecosystem. It integrates the prestigious Goldman Sachs DCF framework with the comprehensive Wind Evaluator structure to produce institutional-grade financial artifacts. This skill automates the tedious aspects of valuation, such as historical data ingestion, balance sheet balancing, and multi-scenario sensitivity analysis.
By leveraging advanced Python scripts, it transforms raw ticker symbols into detailed Excel-based valuation models. The tool covers everything from revenue driver assumptions and CAPM-based WACC calculations to Football Field visualizations, providing a rigorous quantitative foundation for investment decision-making.
Forecast & Valuation Skill Use Cases
- Automating the generation of full 5-year financial forecasts including Income Statements, Balance Sheets, and Cash Flow Statements.
- Performing Discounted Cash Flow (DCF) valuations with automated WACC calculation and terminal growth sensitivity.
- Conducting relative valuation analysis by automatically screening and comparing 8-12 peer companies.
- Generating 'Football Field' charts to visualize valuation ranges across multiple methodologies.
- Validating the reasonableness of financial projections against industry benchmarks and historical trends.
How Forecast & Valuation Skill Works
- Data Ingestion: The skill fetches 5-10 years of historical financial data from sources like Gangtise or Tushare.
- Assumption Modeling: It applies user-defined or industry-standard assumptions for revenue growth, margins, and capital allocation.
- 3-Statement Linking: A 5-year forecast is generated with automated balance sheet balancing logic.
- Valuation Calculation: The engine computes intrinsic value via DCF (using WACC and FCF) and relative value via peer multiples (PE, PB, EV/EBITDA).
- Sensitivity & Output: It runs sensitivity matrices and scenario analyses, then exports the entire model into a professionally formatted Excel file.
Forecast & Valuation Skill Setup
To get started with this skill within Openclaw Skills, ensure you have Python 3.8+ and the required dependencies installed:
# Install dependencies
pip install openpyxl pandas requests numpy
# Configure your API tokens
python3 scripts/configure.py
You will need to update your config.json with valid GANGTISE or TUSHARE tokens to enable automated data fetching.
Forecast & Valuation Skill Data Schema & Taxonomy
The skill produces a multi-sheet Excel workbook with the following metadata and structure:
| Sheet Name | Content Description |
|---|---|
| Cover | Company info, ticker, industry, and analyst details. |
| Historicals | 5-10 years of historical financial statements and key ratios. |
| Assumptions | Drivers for revenue, margins, CAPEX, and working capital. |
| 3-Statement Forecast | Linked Income Statement, Balance Sheet, and Cash Flow projections. |
| Valuation (DCF/Comps) | WACC calculation, FCF projections, and comparable company analysis. |
| Football Field | Visual summary of valuation ranges and current price positioning. |
| Quality Check | Automated sanity checks on growth rates and margin trends. |
Forecast & Valuation Skill Advanced Features
- Multi-source data integration supporting Gangtise, Tushare, and manual entry overrides.
- Automated peer screening of 8-12 comparable companies based on industry classification.
- Built-in sensitivity matrix (WACC vs. Terminal Growth) and Tornado charts for risk assessment.
- Automatic 'Balance Sheet Check' to ensure internal consistency of forecasted statements.
- Support for Baidu Netdisk cloud uploads for easy report sharing.
SKILL.md
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