An AI-powered forecasting engine that systematically maps future scenarios and probabilities from multi-source data to provide actionable strategic advice.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install scenario-forecaster
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 scenario-forecaster 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 Scenario Forecaster?
Scenario Forecaster is a sophisticated analytical engine designed for the Openclaw Skills ecosystem that transforms raw data into strategic foresight. By synthesizing information from diverse sources including news, academic journals, and government reports, it eliminates guesswork in complex decision-making. The skill employs a rigorous cross-validation process to ensure data integrity before applying structural frameworks like PESTEL analysis and Bayesian logic to project likely outcomes.
This tool is particularly valuable for professionals who need to navigate uncertainty. It doesn't just predict a single future; it maps multiple logically consistent paths, allowing users to understand potential triggers and milestones before they occur. By integrating this into your workflow via Openclaw Skills, you gain access to high-level strategic intelligence that was previously the domain of specialized consultancy firms.
Scenario Forecaster Use Cases
- Investors predicting market reactions to central bank decisions or global economic shifts.
- Corporate managers preparing for technological disruptions or industry-wide regulatory changes.
- Policy researchers forecasting the long-term societal effects of new government legislation.
- Risk management teams assessing geopolitical stability and potential supply chain vulnerabilities.
How Scenario Forecaster Works
- Multi-source data gathering across news, social media sentiment, academic papers, and government databases.
- Rigorous cross-validation of facts with confidence ratings across at least two independent sources.
- Structured analysis of drivers using the PESTEL framework (Political, Economic, Social, Technological, Environmental, Legal).
- Scenario mapping to construct 3-5 distinct, logically self-consistent future paths.
- Bayesian-informed probability assessments assigned to each projected scenario for mathematical grounding.
- Generation of role-specific action checklists and recommendations for stakeholders.
Scenario Forecaster Setup
To utilize this skill within the Openclaw Skills environment, you need to provide a structured prompt or configuration object. Use the following format to initiate a forecast:
Forecast: [Your event or topic here]
Time Horizon: [e.g., 6 months, 2 years]
Focus: [e.g., Economic, Technological, Political]
Paths: [Number of scenarios, default is 3]
Ensure your AI agent has access to the web-search tools required to populate the multi-source data streams.
Scenario Forecaster Data Schema & Taxonomy
The Scenario Forecaster organizes data into a structured intelligence report with the following metadata and parameters:
| Parameter | Type | Description |
|---|---|---|
| event_description | string | The core subject or event to be analyzed |
| time_horizon | string | The duration of the forecast (defaults to 6 months) |
| focus_dimensions | list | PESTEL categories to prioritize during analysis |
| num_paths | integer | Number of scenario paths to generate (typically 3-5) |
| confidence_rating | float | A score derived from the cross-validation of source data |
Scenario Forecaster Advanced Features
- Bayesian probability modeling for mathematically sound uncertainty estimation in every scenario.
- Automated fact cross-verification across 2+ independent sources to prevent AI hallucinations.
- Tailored persona-based outputs providing specific 'do and don't' lists for investors, managers, and policymakers.
- Multi-dimensional driver identification using a full-spectrum PESTEL framework integrated into the Openclaw Skills logic.
SKILL.md
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