Solar Skills
Claude Code skills for exporting and AI-driven analysis of residential solar PV system data. Export skills pull hourly data from your inverter's cloud platform (SolisCloud or Deye/Solarman), then the analyze skill produces a consultant-style report.
Disclaimer
This tool is AI-assisted. While the underlying data (CSV export) comes directly from your inverter's API, the analysis report — including recommendations, projections, and financial estimates — is generated by an AI model. Results may contain inaccuracies, misinterpretations, or errors. Always verify critical findings (especially financial and equipment-related decisions) against your own records, manufacturer specifications, or a qualified solar professional before acting on them.
Why Bother?
Most solar monitoring apps show you charts but don't tell you what to do. This tool bridges that gap. The heavy lifting — aggregation, EV day detection, efficiency calculations, bill modelling — is done by a deterministic Python script, not the AI. The AI's role is interpreting those numbers in context: writing the narrative, inferring seasonal factors from your location, and tailoring recommendations to your setup. It won't replace a professional audit, but it surfaces patterns you'd likely miss scrolling through dashboards — and it's a solid starting point for deciding where to focus.
Skills
/solar-skills:export-hourly-soliscloud
Exports monthly 5-minute interval data from a SolisCloud-monitored solar+battery system and aggregates it into hourly CSV files.
- Two export methods: API (primary, ~15s/month) or Chrome browser automation (fallback)
- Pure Python stdlib — no pip dependencies for the API method
- Outputs standardized CSV with PV, battery, grid, load, and SOC columns
/solar-skills:export-hourly-deye
Exports monthly 5-minute interval data from a Deye inverter via the Solarman Open API and aggregates it into hourly CSV files.
- API-only — direct REST API calls via Solarman Open API, no browser needed
- Auto-discovery — finds station and device automatically if not configured
- Pure Python stdlib — no pip dependencies
- Outputs standardized CSV with PV, battery, grid, load, and SOC columns
/solar-skills:analyze
AI-driven analysis of the exported hourly CSV data that produces a detailed performance report with actionable recommendations.
- Monthly totals, EV day detection, hourly patterns, weekday/weekend analysis
- System sizing evaluation, battery health, inverter clipping detection
- Bill impact with flat/tiered/TOU tariff support, ROI calculation
- Anomaly detection, annual projection, carbon offset estimation
- 180-test suite included
Analysis Coverage
The /solar-skills:analyze report covers the following areas, written in a consultant narrative style with quantified findings and actionable guidance:
| Category | What it covers |
|---|
| Monthly Totals | PV generation, consumption, grid import/export, battery throughput, self-consumption and self-sufficiency rates |
| EV Day Detection | Adaptive threshold detection of EV/PHEV charging days; splits all subsequent analysis into EV vs non-EV patterns |
| Hourly Profiles | Average PV, battery, grid, load, and SOC by hour; identifies peak generation, peak load, export windows, and battery taper timing |
| Weekday vs Weekend | Load shape comparison, self-sufficiency differences, occupancy-driven optimization opportunities |
| System Sizing | PV capacity factor, inverter clipping detection, DC/AC ratio, PV-to-load ratio, sizing verdict (undersized / correct / oversized) |
| Battery Health | Usable vs nominal capacity, round-trip efficiency, daily cycle depth, annual cycle projection, remaining cycle life estimate |
| Additional Panels | Hour-by-hour simulation of extra PV capacity, marginal self-consumption vs export split, inverter headroom check |
| Peak Demand | Peak grid draw and PV output with timestamps, EV vs non-EV day comparison |
| Anomaly Detection | PV shortfalls vs rolling baseline, unusual load spikes, battery efficiency outliers |
| Bill Impact | Flat, tiered, or TOU tariff modelling; monthly cost table; with-vs-without-solar comparison; feed-in credits |
| ROI Estimation | Payback period with 0.5%/yr panel degradation, 25-year lifetime savings, with-vs-without-battery comparison |
| Month-over-Month Trends | PV, load, self-sufficiency, and battery efficiency trends across months; flags significant shifts |
| Annual Projection | Latitude-based seasonal adjustment, de-seasonalized baseline, generation forecast at years 1/10/25 |
| Carbon Offset | Locale-based grid emission factor, annual CO₂ avoided, tree and driving equivalents |
| Best / Worst Days | Highest and lowest self-sufficiency days with full metrics and causal explanation |
