2.6k
GitHub Stars
3
Bundled Files
3 weeks ago
Catalog Refreshed
2 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstart where the catalogue uses aiagentskills.
npx veilstart add skill openclaw/skills --skill solar-weather- _meta.json292 B
- SKILL.md4.4 KB
- solar-weather.py10.1 KB
Overview
This skill monitors real-time solar weather using NOAA Space Weather Prediction Center data to track solar flares, geomagnetic storms, aurora forecasts, solar wind, and active alerts. It provides current conditions, a 3-day forecast, aurora outlooks, solar wind metrics (Bt/Bz), and concise summaries. The tool is lightweight, script-driven, and returns JSON for integration or human-readable output for quick decision-making.
How this skill works
The skill queries NOAA SWPC endpoints for real-time status, alerts, and solar wind measurements and parses official scales (R, S, G) to produce readable severity levels. Commands return current conditions, a three-day probability forecast, aurora likelihood based on Bz and geomagnetic indices, and active watch/warning messages. Adding --json yields structured output for automation or dashboards.
When to use it
- Before HF radio contests or operations to check for radio blackouts (R-scale).
- Planning night photography or aurora chases to assess aurora likelihood and Bz trends.
- Monitoring satellite operations or power grid risk during elevated S- or G-scale activity.
- Automating alerts or dashboards that need up-to-date space weather metrics.
- Checking active NOAA watches, warnings, and alerts in real time.
Best practices
- Check current and aurora commands frequently during active periods — conditions change hourly.
- Use the Bz component (negative values, especially < -5 nT) plus G-scale to evaluate aurora potential.
- Combine aurora outlooks with local cloud cover and darkness windows for visibility planning.
- Enable --json output for integration with monitoring systems or mobile notifications.
- Treat NOAA SWPC as the authoritative source — use this skill to parse and present that data clearly.
Example use cases
- Ham radio operator verifying R-scale before a contest to avoid HF disruptions.
- Aurora chaser checking short-term Bz trends and a 3-day forecast to pick the best night.
- Satellite operator monitoring S-scale and solar wind for anomaly risk during passes.
- Power grid operator receiving alerts when geomagnetic storms reach levels that could affect infrastructure.
- Developer piping --json output into a dashboard or automated notification workflow.
FAQ
Forecasts use NOAA indices and solar wind Bz to estimate likelihood; they are probabilistic and best paired with local weather and real-time Bz monitoring.
Can I automate alerts?
Yes. Use --json output with a scheduler or monitoring system to trigger notifications when thresholds (e.g., G1 or Bz < -5 nT) are crossed.
Where does the data come from?
All data is pulled from NOAA Space Weather Prediction Center (SWPC) real-time feeds and official watch/warning services.