PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add rules/mohitagw15856/pm-claude-skills/ev-vs-gasWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ev-vs-gas)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ev-vs-gas"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ev-vs-gas/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ev-vs-gas"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ev-vs-gas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.01213 |
| Opus 5 | $0.00050 | $0.00607 |
| Sonnet 5 | $0.00020 | $0.00243 |
| Haiku 4.5 | $0.00010 | $0.00121 |
Grade A, and why
ev-vs-gas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EV vs Gas Skill
The EV question is a crossover question: a higher price tag melting under lower running costs, with the answer living in your numbers — miles driven, electricity price (home vs. public charging are different products), and how long you keep cars. This skill runs the cumulative math year by year and reports where the lines cross, then is honest about what the model can't see: battery risk, a resale market still finding its feet, and the charger install that belongs in the purchase price.
What This Skill Produces
- The cumulative table — EV vs gas total cost per year over the horizon, from the script
- The crossover year — where the EV pulls ahead, or the finding that it doesn't within the horizon
- The per-year energy math — miles ÷ efficiency × price, both sides, shown not asserted
- The not-modeled list — battery, resale, charger install, price drift — stated up front
Required Inputs
Ask for these if not provided:
- The two candidates' prices — the actual EV and the comparable gas car (same class — comparing a luxury EV to an economy gas car answers a different question); applicable incentives, flagged as verify-eligibility
- Annual miles — the single biggest lever; low-mileage drivers should see how far the crossover moves
- Electricity reality — home charging rate if they have it, or an honest blended rate if they'd rely on public charging (often 2–3× home rates — it can erase the fuel advantage; say so)
- Ownership horizon — a crossover at year 6 means opposite things to a 3-year and a 10-year keeper
Programmatic Helper
python3 scripts/ev_vs_gas.py --ev-price 42000 --gas-price 33000 --incentive 7500
python3 scripts/ev_vs_gas.py --ev-price 42000 --gas-price 33000 --miles 15000 --kwh-price 0.16 --gas-price-gal 3.60 --json
Deterministic. Defaults are labeled and overridable: 3.3 mi/kWh, 32 mpg, $0.15/kWh, $3.50/gal, maintenance $500 vs $900, +$150/yr EV insurance. Home-charger install cost belongs added to --ev-price.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 80 lines · 100 tokens per session scan A 6b610f6a81b4
ev-vs-gas is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 100 tokens to every session and 1,213 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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