Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jeffreytse/grimoire-core --skill review-used-car-listinggit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/skills/jeffreytse/grimoire-core/review-used-car-listing)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/review-used-car-listing"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/review-used-car-listing/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/skills/jeffreytse/grimoire-core/review-used-car-listing"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/review-used-car-listing.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 | $0.00030 | $0.01295 |
| Opus 5 | $0.00015 | $0.00647 |
| Sonnet 5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
review-used-car-listing 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 12d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Used Car Listing
Systematically evaluate a used car listing to spot red flags, verify pricing, and determine whether a vehicle merits an in-person inspection.
Why This Is Best Practice
Adopted by: Consumer Reports used-car methodology, CarFax buyer guidance, AAA car-buying program, NHTSA consumer protection recommendations
Impact: 1 in 3 online used-car listings contains misleading information (FTC used-car rule enforcement data); buyers who screen listings before visiting save 3–5 hours per vehicle; systematic listing review eliminates 60–70% of unsuitable vehicles before travel; average overpayment without market comparison is $1,200–$3,500 (Edmunds pricing data)
Why best: Online listings have zero cost to post and little consequence for exaggeration; systematic screening using objective data sources (pricing databases, VIN reports, recall databases) creates an objective filter before emotional engagement with the vehicle
Sources: NHTSA VIN lookup and recall database; CarFax/AutoCheck "How to Read a Report" guide; Consumer Reports "Used Car Reliability Ratings"; FTC Used Car Rule
Steps
-
Verify the VIN is present and valid — A 17-character VIN should be listed; run it at NHTSA's vinsearch.nhtsa.dot.gov to confirm year/make/model matches the ad. No VIN = do not contact — either fraud or seller hiding history.
-
Check for open safety recalls — Enter the VIN at nhtsa.gov/recalls; open recalls must be disclosed under FTC rules; unfixed critical recalls (brake, steering, airbag) are a safety reason to skip the vehicle unless the seller commits to fixing before sale.
-
Assess the price against market data — Pull the vehicle's price range from three sources: Edmunds Instant Market Value (IMV), KBB Private Party Value, and local comparable listings on CarGurus or AutoTrader. A price 10%+ below market requires explanation; a price 15%+ above market requires exceptional justification.
-
Analyze the listing photos — Count photos: fewer than 12 is a red flag; professional listings show 20–40. Must-have photos: all four corners of exterior, engine bay, all four tires/wheels, dashboard with odometer, front and rear seats, cargo area, undercarriage if available. Missing interior or undercarriage photos suggest hiding damage.
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.
- 12d ago First seen · 60 lines · 30 tokens per session scan A 0c087fd2365e
review-used-car-listing is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 23d ago), licensed MIT. It adds 30 tokens to every session and 1,295 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
semantic-model-disambiguation
Analyze Power BI semantic models for column-level overlaps that confuse Copilot and Fabric data agents. Detect ambiguity, review with domain expert, apply fixes via MCP.
prompt-2-data
Generate comprehensive synthetic relational data for any specified subject with multiple normalized CSV files maintaining referential integrity.
infrastructure-as-code
Domain: DevOps & Cloud Engineering.
academic-paper-drafting
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
research-first-development
Build knowledge bases that build software — research before code, teach before execute.
agent-governance
Patterns for adding safety, trust, and policy enforcement to AI agent systems -- control which tools agents can call, what content they process, and maintain accountability through audit trails.