An advanced AI-driven research and forecasting skill that runs multi-agent pipelines to generate graded, evidence-backed probabilistic estimates.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install axion
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install axion using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Axion Forecasting is a high-performance skill designed to calculate the precise probabilities of future events, macroeconomic shifts, business deals, and technological trends. By integrating this skill with Openclaw Skills, developers can deploy a coordinated swarm of specialized research agents. These agents scrape live web search indices, evaluate market metrics, dissect SEC filings, and formulate Fermi estimates to output deep-dive quantitative forecasts.
Rather than generating simple heuristics, Axion operates on an asynchronous model. It crawls multiple sources, parses real-world evidence, and generates highly detailed probabilistic metrics. This structured output is ideal for risk analysis, strategic planning, financial models, and any decision-making process that requires quantifiable risk assessment rather than static text predictions.
To deploy the Axion forecasting skill, complete the following setup steps:
AXION_API_KEY variable:export AXION_API_KEY="your_axion_api_token_here"
The forecasting agents will automatically detect this environment variable to sign outgoing HTTPS requests.
The Axion forecasting skill uses structured JSON payloads to manage query limits and detail results. Below is the primary schema layout:
| Parameter | Data Type | Required | Description |
|---|---|---|---|
input |
String | Yes | The target query or future event to analyze (e.g., 'Will the Fed cut rates in June 2026?'). |
effort |
String | No | Computation depth setting: low, medium, or high. |
max_forecasts |
Integer | No | Range 1-10. Spawns multiple parallel forecast evaluations to broaden predictions. |
When a forecast transitions to a completed state, the skill returns an array of structured objects featuring:
probability: Float value between 0 and 1 expressing the calculated event probability.confidence_lower / confidence_upper: Float values setting the confidence intervals of the prediction.forecast_text: Descriptive technical evaluation summarizing the predicted outcome.reasoning: The core analytical logical steps and cited sources that validate the calculation.resolution_date: Expected calendar date when the target scenario will resolve.low/medium/high) and parallel streams (up to 10 concurrent runs) to match search depth criteria.Loading
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