A sophisticated toolset for applying quantitative and qualitative forecasting techniques to demand planning, revenue projections, and trend analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install forecasting
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 forecasting 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 Forecasting?
The Forecasting skill is designed for data-driven decision-making within the Openclaw Skills ecosystem. It provides AI agents with a structured framework to execute complex predictive modeling, ranging from simple moving averages to causal regression analysis and long-term Delphi methods. By bridging the gap between raw historical data and actionable future insights, this skill enables precise trend analysis and risk assessment.
This skill is particularly valuable for organizations needing to navigate uncertain environments through scenario planning. It ensures that every projection is backed by a clear rationale, a defined confidence interval, and a rigorous evaluation of underlying assumptions. Whether you are managing supply chains or projecting quarterly growth, this tool provides the mathematical and qualitative rigor required for professional-grade forecasting.
Forecasting Use Cases
- Projecting future sales and revenue based on historical quarterly growth.
- Demand planning for inventory management to prevent stockouts or overstocking.
- Identifying long-term market trends and shifts using expert consensus via the Delphi Method.
- Assessing causal relationships between marketing spend and lead generation through regression modeling.
- Developing contingency plans for high-uncertainty environments using scenario planning.
How Forecasting Works
- The AI agent identifies a forecast trigger such as a request for a projection, prediction, or trend analysis.
- The skill analyzes the input data and environment to select the most appropriate forecasting method (e.g., Exponential Smoothing for trending data).
- Historical data is processed to establish a baseline and calculate historical error rates for model validation.
- Quantitative models generate future period results, including lower and upper bounds for statistical confidence.
- Qualitative factors, including assumptions and potential risks, are integrated into the final report to provide context and mitigation strategies.
Forecasting Setup
To integrate this forecasting capability into your environment, ensure your agent has access to the Openclaw Skills library. You can initialize the skill configuration via the CLI:
# Install the Openclaw Skills package
npm install @openclaw/forecasting
# Configure the forecasting parameters
openclaw skill enable forecasting
Forecasting Data Schema & Taxonomy
The Forecasting skill organizes its outputs into a standardized schema to ensure consistency across different Openclaw Skills deployments:
| Data Component | Description | Format |
|---|---|---|
| Method Selection | Documentation of the chosen algorithm and rationale | Markdown Section |
| Historical Performance | Table of actual vs. forecast values with error tracking | Markdown Table |
| Forecast Projections | Predictive values including Confidence Intervals (90%, 85%, etc.) | Markdown Table |
| Risk Matrix | Identification of probability, impact, and mitigation actions | Markdown Table |
| Assumptions | List of environmental or internal factors influencing the model | Unordered List |
Forecasting Advanced Features
- Multi-method validation to compare results across different mathematical models for higher accuracy.
- Dynamic confidence interval adjustment based on data volatility and time horizon.
- Automated risk mitigation planning linked to specific forecast variances.
- Support for seasonality adjustments to refine short-term demand planning accuracy.
SKILL.md
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