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/car-tcoWrote 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/car-tco)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/car-tco"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/car-tco/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/car-tco"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/car-tco.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 | $0.00101 | $0.01020 |
| Opus 5 | $0.00051 | $0.00510 |
| Sonnet 5 | $0.00020 | $0.00204 |
| Haiku 4.5 | $0.00010 | $0.00102 |
Grade B, and why
car-tco scanned grade B with 1 finding 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 8d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- [ ] Do not moralize the want — price the options honestly and let the user choose with open eyes How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Car TCO Skill
Cars are sold on monthly payments and owned on total cost — and the two rank options differently. Depreciation (the biggest cost of a new car) never appears on a statement; maintenance (the biggest fear about an old car) is usually smaller than a year of new-car depreciation. This skill compares buy-new, buy-used, lease-forever, and keep-current on the same total-cost basis over the same horizon.
What This Skill Produces
- Ranked scenario totals — every requested scenario over the same horizon, cheapest first
- True per-month cost — total ÷ months, the number to compare against the payment the dealer quotes
- The assumption ledger — depreciation curve, maintenance ramp, insurance deltas, all labeled
- The not-modeled list — financing interest, jurisdiction taxes, reliability luck
Required Inputs
Ask for these if not provided:
- Which scenarios to compare — any of: new price, used-equivalent price, lease terms, current car's value + annual maintenance
- Horizon — years they realistically keep cars (default 8, labeled); short horizons flatter leasing, long ones flatter buying
- Miles per year and rough fuel/energy cost (defaults 12,000 mi / $0.14 per mile, labeled)
Programmatic Helper
python3 scripts/car_tco.py --new-price 38000 --used-price 24000 --lease-month 420 --keep-value 9000
python3 scripts/car_tco.py --new-price 38000 --keep-value 9000 --keep-maint 1800 --horizon 6 --json
Deterministic. Depreciation: 20% year one then 10%/yr for new, gentler for used/current. Maintenance ramps 8%/yr (used cars start further up the ramp). Leases re-lease at each term end with a fresh drive-off. Resale value is credited back — TCO is what you spent minus what you can recover.
Framework: The Payment Illusion Rules
- Depreciation is the invisible line item — a new car's largest cost has no bill; it's the resale-value credit shrinking
- "My old car needs $2,000 of work" is usually cheap — compare the repair to a year of the replacement's depreciation before calling it "not worth fixing"
- Leasing buys flexibility, not savings — lease-forever means paying peak depreciation years forever; price that honestly and let people buy flexibility knowingly
- Margins under ~10% are noise — the model's assumptions can't resolve differences that small; say "roughly a tie," not a winner
- The horizon drives the ranking — always name it; the same inputs rank differently at 3 years vs 10
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.
- 8d ago First seen · 76 lines · 101 tokens per session scan B d70ab0b33653
car-tco is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 1,020 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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