A specialized aurora forecasting workflow providing localized viewing chances and travel recommendations for Dereham and the North Norfolk coast.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aurora-norfolk
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 aurora-norfolk 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 Aurora Norfolk?
Aurora Norfolk is a precision-engineered planning and live-update workflow designed to provide actionable intelligence for spotting the Northern Lights. By integrating real-time space weather data with hyper-local cloud forecasts, this skill removes the guesswork from aurora hunting, specifically comparing inland conditions in Dereham against the superior northern horizon of the Norfolk coast.
As part of the Openclaw Skills ecosystem, it prioritizes practical viewing percentages over raw scientific data. It evaluates a blend of geomagnetic activity, light pollution, moon interference, and sky clarity to issue clear travel recommendations, ensuring users only head out when there is a genuine, actionable chance of a sighting.
Aurora Norfolk Use Cases
- Planning aurora photography trips over a rolling 3-day window.
- Receiving real-time live-night updates when geomagnetic conditions spike during active viewing hours.
- Comparing inland vs. coastal visibility to decide if a drive to the North Norfolk coast is necessary.
- Automating daily morning briefings to check for upcoming solar storm impacts.
How Aurora Norfolk Works
- The skill identifies the current local-night period using the Europe/London timezone.
- It fetches solar wind and geomagnetic data from NOAA SWPC and AuroraWatch UK to assess aurora intensity.
- It cross-references this with Met Office site-specific cloud and visibility data for Dereham and coastal points.
- A mental decision model calculates a 'practical visible chance' based on darkness, moon penalty, and horizon advantage.
- The workflow assigns a status (from no_watch to high_alert) and a specific travel action.
- It generates a formatted update—ranging from 3-day tables to urgent change alerts—based on the current automation stage.
Aurora Norfolk Setup
To integrate this skill into your local environment, ensure you have the Openclaw Skills framework installed and your API keys for weather services configured.
# Add the skill to your Openclaw configuration
openclaw install aurora-norfolk
# Configure a daily cron job for the 3-day outlook
openclaw schedule "0 9 * * *" --skill aurora-norfolk --mode outlook
Aurora Norfolk Data Schema & Taxonomy
The skill organizes its output into structured templates to ensure consistency across different notification triggers:
| Data Component | Description | Format |
|---|---|---|
| Status Thresholds | Categorizes risk from no_watch (<20%) to high_alert (>55%) | String |
| Travel Actions | Specific recommendations: stand_down, watch, go_local, or go_coast | Enum |
| Forecast Table | Comparative 3-night outlook for inland and coastal locations | Markdown Table |
| Confidence Level | Assessment of data reliability: low, medium, or high | String |
| Visual Outcome | Predicts if the aurora will be camera-only or visible by eye | String |
Aurora Norfolk Advanced Features
- Alert-Day Escalation: Automatically switches from daily checks to 15-minute live updates during high-activity nights.
- Intelligent Noise Suppression: Only triggers change alerts if the viewing chance shifts by 10% or the best window moves by more than an hour.
- Multi-Location Comparison: Applies a 'coastal bonus' logic to determine if the northern horizon advantage justifies travel.
- Low-Temperature Agent Optimization: Designed to run with high precision and minimal hallucination for scientific data interpretation.
SKILL.md
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