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在 OpenClaw 中安装
/install forecasting
功能描述
Apply quantitative and qualitative forecasting techniques. Use for demand planning, revenue projections, and trend analysis.
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install forecasting - 安装完成后，直接呼叫该 Skill 的名称或使用
/forecasting触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
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.
元数据
常见问题
Forecasting 是什么？
Apply quantitative and qualitative forecasting techniques. Use for demand planning, revenue projections, and trend analysis. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 214 次。
如何安装 Forecasting？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install forecasting」即可一键安装，无需额外配置。
Forecasting 是免费的吗？
是的，Forecasting 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Forecasting 支持哪些平台？
Forecasting 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Forecasting？
由 linuszz（@linuszz）开发并维护，当前版本 v1.0.0。
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