Agent skill · AI & Agents

solar-breakeven

Model whether solar panels pay for themselves for your roof — net cost after incentives, bill offset with degradation, electricity inflation, the inverter replacement, and the breakeven year, plus the policy risk no calculator controls. Use when asked are solar panels worth it, when does solar break even, check this solar quote's payback claim, or model solar for my bill. Produces the year-by-year table from the script, the breakeven year, the quote-vs-model comparison, and the not-modeled list led by net-metering risk.

mohitagw15856github.com/mohitagw15856GitHub ↗
claude-codecursorships scriptsMIT
Install
npx skills add mohitagw15856/pm-claude-skills --skill solar-breakeven --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: yes
Path: skills/solar-breakeven/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,255
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Solar Breakeven Skill Every solar quote comes with a payback claim, and the claim always assumes the sunny version: full incentive eligibility, generous net metering forever, zero maintenance. This skill runs the honest model — net cost after *verified* incentives, offset that degrades ~0.5%/yr, electricity prices that inflate, and the inverter that dies around year 12 — and reports the breakeven year with its assumptions labeled. The biggest risk stays outside every model and gets named instead: net-metering policy is a regulatory decision that can change under you, and it moves paybacks by years. ## What This Skill Produces - **The year-by-year table** — savings, cumulative, vs-cost — from the script - **The breakeven year** — and the net gain at the 25-year warranty horizon - **The quote check** — the installer's payback claim vs. this model, with the assumption gaps named - **The not-modeled list** — net-metering risk first, financing interest, roof interactions ## Required Inputs Ask for these if not provided: - **The quote** — installed cost, claimed incentives (flagged verify-eligibility — incentives have income, tax-liability, and program caps), the claimed payback for co

What's inside
Steps it walks through
  1. What This Skill Produces
  2. Required Inputs
  3. Programmatic Helper
  4. Framework: The Honest-Model Rules
  5. Output Format
  6. The Table and the Breakeven
  7. Quote Check
  8. Sensitivity
  9. What This Model Ignores
  10. Quality Checks
  11. Anti-Patterns
Ships with 1 file
  • scripts/solar_breakeven.py
Commands it runs
python3 scripts/solar_breakeven.py --cost 22000 --incentive 6600 --bill 190
python3 scripts/solar_breakeven.py --cost 22000 --incentive 6600 --bill 190 --offset 90 --json
More from pm-claude-skills
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About this skill
What does the solar-breakeven skill do?

Model whether solar panels pay for themselves for your roof — net cost after incentives, bill offset with degradation, electricity inflation, the inverter replacement, and the breakeven year, plus the policy risk no calculator controls. Use when asked are solar panels worth it, when does solar break even, check this solar quote's payback claim, or model solar for my bill. Produces the year-by-year table from the script, the breakeven year, the quote-vs-model comparison, and the not-modeled list led by net-metering risk.

How do I install it?

Run `npx skills add mohitagw15856/pm-claude-skills --skill solar-breakeven --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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