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 agentmods add rules/onesimplecode/agent-engineering-standards/data-privacygit clone --depth 1 https://github.com/onesimplecode/agent-engineering-standardsWhat 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 | $0.00082 | $0.00082 |
| Opus 5 | $0.00041 | $0.00041 |
| Sonnet 5 | $0.00016 | $0.00016 |
| Haiku 4.5 | $0.00008 | $0.00008 |
Grade A, and why
data-privacy 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 yesterday.
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.
What it actually says
Data Privacy
TR-SEC-002 — PII encrypted at rest
Personally identifiable information must be encrypted at rest when stored by an application.
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.
- yesterday First seen · 13 lines · 82 tokens per session scan A 210e661ea4fa
data-privacy is a cursor rule published in the GitHub repository onesimplecode/agent-engineering-standards (3 stars, last pushed 4d ago), licensed MIT. It adds 82 tokens to every session, about $0.0004 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 cursor rules, from other repositories
always-apply-string
Rule with string alwaysApply.
cursorrules
When the user asks about lead scoring, use lead-scoring-ai-mcp tools: scorelead, addlead, updateleadactivity, getleadscore, getallleads.
cursorrules
When the user asks about web research, use web-research-mcp tools: websearch, browsepage, extractarticle, researchtopic, getweather.
cursorrules
When the user asks about quiz generator, use quiz-generator-ai-mcp tools: generatequiz, validateanswers, generateflashcards, assessdifficulty.
cursorrules
When the user asks about landlaw, use landlaw-ai-mcp tools: searchlandregistry, checkplanningpermission, explaincovenant, calculatesdlt, draftsectionnotice.
cursorrules
When the user asks about text stats, use text-stats-ai-mcp tools: wordcount, readingtime, keyworddensity, sentimentscore.