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/install forecasting
Description
Apply quantitative and qualitative forecasting techniques. Use for demand planning, revenue projections, and trend analysis.
README (SKILL.md)
Forecasting Techniques
Metadata
- Name: forecasting
- Description: Quantitative and qualitative forecasting methods
- Triggers: forecast, projection, prediction, demand planning, trend analysis
Instructions
Apply appropriate forecasting techniques for $ARGUMENTS.
Framework
Forecasting Methods
| Method | Best For | Time Horizon | Accuracy |
|---|---|---|---|
| Moving Average | Stable demand | Short | Medium |
| Exponential Smoothing | Trending data | Short-Medium | High |
| Regression | Causal relationships | Medium-Long | High |
| Scenario Planning | Uncertain environment | Long | Medium |
| Delphi Method | Expert consensus | Long | Medium |
Output
## Forecast: [Subject]
### Method Selection
**Chosen Method**: [Method name]
**Rationale**: [Why this method]
### Historical Data
| Period | Actual | Forecast | Error |
|--------|--------|----------|-------|
| Q1 | 100 | - | - |
| Q2 | 110 | 105 | +5 |
| Q3 | 115 | 112 | +3 |
| Q4 | 120 | 118 | +2 |
### Forecast Results
| Period | Forecast | Lower Bound | Upper Bound | Confidence |
|--------|----------|-------------|-------------|------------|
| Q1 Next | 125 | 118 | 132 | 90% |
| Q2 Next | 130 | 120 | 140 | 85% |
| Q3 Next | 135 | 122 | 148 | 80% |
### Assumptions
1. [Assumption 1]
2. [Assumption 2]
3. [Assumption 3]
### Risks
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| [Risk 1] | Medium | High | [Action] |
| [Risk 2] | Low | Medium | [Action] |
Tips
- Use multiple methods for validation
- Document assumptions clearly
- Update forecasts with new data
- Consider seasonality
Usage Guidance
This skill appears internally consistent and low-risk because it is instruction-only and requests no credentials or installs. However, the instructions are intentionally general—when you use it, provide explicit, non-sensitive sample data and clear constraints (time horizon, required confidence intervals, allowed data sources). Avoid pasting secrets (API keys, raw database extracts), and consider testing outputs on synthetic data first. If you are concerned about autonomous runs, keep using user-invocation only or monitor the agent's requests for additional context before granting access to real datasets. If the skill later adds install steps, environment variables, or file-system access, reassess immediately.
Capability Analysis
Type: OpenClaw Skill
Name: forecasting
Version: 1.0.0
The forecasting skill bundle is purely informational, providing a framework and output template for the AI agent to perform trend analysis and demand planning. It contains no executable code, external dependencies, or instructions that could lead to data exfiltration or unauthorized system access.
Capability Assessment
Purpose & Capability
Name/description (forecasting, demand/revenue projections) align with the SKILL.md content: methods, selection guidance, and an output template. The skill does not request unrelated binaries, credentials, or config.
Instruction Scope
Runtime instructions are minimal and generic ('Apply appropriate forecasting techniques for $ARGUMENTS') and include a useful output template. They do not instruct the agent to read system files, environment variables, or send data externally, but the wording is open-ended and gives the agent broad discretion about what input/context to use—so you should be explicit when providing data and context.
Install Mechanism
No install spec and no code files (instruction-only). This is lowest-risk: nothing will be written to disk or downloaded by the skill itself.
Credentials
The skill declares no required environment variables, credentials, or config paths. There is no disproportionate access requested relative to forecasting functionality.
Persistence & Privilege
always:false (default) and no requests to modify agent/system settings. The skill can be invoked autonomously per platform defaults, which is normal for skills of this type.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install forecasting - After installation, invoke the skill by name or use
/forecasting - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the "forecasting" skill.
- Provides a framework for quantitative and qualitative forecasting methods.
- Includes guidance on method selection, output structure, and risk assessment.
- Supports use cases such as demand planning, revenue projections, and trend analysis.
- Offers tips for validation, documentation, and data updates.
- Outlines common forecasting techniques with their best use cases and accuracy levels.
Metadata
Frequently Asked Questions
What is Forecasting?
Apply quantitative and qualitative forecasting techniques. Use for demand planning, revenue projections, and trend analysis. It is an AI Agent Skill for Claude Code / OpenClaw, with 214 downloads so far.
How do I install Forecasting?
Run "/install forecasting" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Forecasting free?
Yes, Forecasting is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Forecasting support?
Forecasting is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Forecasting?
It is built and maintained by linuszz (@linuszz); the current version is v1.0.0.
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