An AI-driven analytical tool for projecting future metric trends, evaluating KPI feasibility, and simulating hypothetical business scenarios.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install forecast-simulation
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-simulation 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 Simulation?
The Forecast Simulation skill is a specialized utility within the Openclaw Skills ecosystem designed to answer the fundamental question: "What happens next?" It acts as the forward-looking layer of the data analysis pipeline, moving beyond historical queries to provide actionable projections. By utilizing mathematical extrapolation methods such as linear regression, moving averages, and factor-based modeling, it allows developers to build agents that can anticipate market changes and resource needs.
This skill is particularly effective for teams that need to transition from simply monitoring data to predicting future outcomes. Whether it is estimating month-end revenue or determining if a specific target is still achievable given current momentum, this skill provides the logical framework to generate these insights with transparency regarding assumptions and uncertainty.
Forecast Simulation Use Cases
- Predicting month-end sales totals based on current daily performance and historical trends.
- Evaluating the feasibility of reaching quarterly KPIs and calculating the required daily average to close the gap.
- Running What-if simulations to see how changes in conversion rates or pricing affect total GMV.
- Forecasting inventory depletion or resource saturation to optimize supply chain and capacity planning.
How Forecast Simulation Works
- The skill identifies the user intent related to future projections, such as "Will we hit our goal?" or "What if we increase traffic?"
- It delegates data collection to the metric-query skill to retrieve necessary historical time-series data or current cumulative totals.
- A local Python environment executes mathematical models like linear regression or weighted moving averages to extrapolate the data.
- For simulation tasks, the skill applies business factor formulas (e.g., Revenue = Traffic x Conversion x Price) to calculate the impact of hypothetical changes.
- The system generates a comprehensive output including point estimates, confidence intervals, and a clear list of underlying assumptions.
Forecast Simulation Setup
To deploy this skill, ensure that the metric-query skill is properly configured and linked, as it serves as the primary data source.
# Install the forecast-simulation skill
openclaw install forecast-simulation
# Configure dependencies for Python-based mathematical execution
pip install numpy
No additional machine learning infrastructure is required, as the skill uses optimized mathematical libraries for trend fitting.
Forecast Simulation Data Schema & Taxonomy
The Forecast Simulation skill structures its analysis by mapping historical inputs to projected outputs across four primary categories:
| Feature | Description | Implementation |
|---|---|---|
| Trend Extrapolation | Uses historical windows (e.g., 30 days) to predict future values. | Linear Regression / Moving Avg |
| Target Gap Analysis | Compares current cumulative totals against a set goal (KPI). | Gap = Target - Current |
| What-if Simulation | Calculates output changes based on adjusted input factors. | Factor-based Formulas |
| Depletion Tracking | Predicts the date a resource will reach zero based on consumption. | Stock / Daily Consumption |
Forecast Simulation Advanced Features
- Support for multiple extrapolation methods including weighted moving averages to prioritize recent data trends.
- Automated confidence interval generation using standard deviation to visualize prediction volatility.
- Multi-factor linkage for complex simulations where multiple variables (like UV and CVR) change simultaneously.
- Dynamic feasibility assessment with status indicators (Success, Warning, Critical) for target achievement tracking.
SKILL.md
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