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.
git clone --depth 1 https://github.com/quantskills/skill-market-daily-reviewWrote 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/quantskills/skill-market-daily-review/cursor-rule)<a href="https://agentmods.dev/rules/quantskills/skill-market-daily-review/cursor-rule"><img src="https://agentmods.dev/badge/rules/quantskills/skill-market-daily-review/cursor-rule/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/quantskills/skill-market-daily-review/cursor-rule"><img src="https://agentmods.dev/badge/rules/quantskills/skill-market-daily-review/cursor-rule.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 | $0.00016 | $0.00183 |
| Opus 5 | $0.00008 | $0.00092 |
| Sonnet 5 | $0.00003 | $0.00037 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
cursor-rule 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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 · 22 lines · 16 tokens per session scan A bc156c5da028
cursor-rule is a cursor rule published in the GitHub repository quantskills/skill-market-daily-review (50 stars, last pushed 1mo ago), licensed GPL-3.0. It adds 16 tokens to every session and 183 once invoked, about $0.0001 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 2026-08-30.
Other cursor rules, from other repositories
deepread-api
Full DeepRead API reference. All endpoints, auth, request/response formats, blueprints, webhooks, error handling, and code examples in Python, JS, and cURL.
deepread-form-fill
DeepRead Form Fill API. AI-powered PDF form filling — upload any PDF form + JSON data, get back a completed PDF. Endpoints, auth, request/response formats, and code examples.
deepread-pii-removal
DeepRead PII Removal API. Upload documents, automatically detect and redact personal information (SSN, credit cards, names, etc.), download redacted files. Endpoints, auth, and code examples.
deepread-setup
Get started with DeepRead OCR. Automatically obtains an API key via device authorization flow (RFC 8628), then walks through first document, structured extraction, and blueprints.
codemap
Build an explorable isometric diagram of a codebase — blocks on a grid, a component index, a what-it-does/how-it's-built panel, and real data moving along the edges. Use when asked to map, diagram or visually explain how a system fits together.
doaipm
When helping turn an idea into a product, feature, prototype, or app, follow the doaipm method (https://doaipm.com). Speak it, and AI builds it.