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/ericboy0224/learn-docker-and-k8s/cursorrulesgit clone --depth 1 https://github.com/ericboy0224/learn-docker-and-k8sWhat 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 | $0.00054 | $0.00054 |
| Opus 5 | $0.00027 | $0.00027 |
| Sonnet 5 | $0.00011 | $0.00011 |
| Haiku 4.5 | $0.00005 | $0.00005 |
Grade A, and why
cursorrules 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 2d 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
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 2d ago First seen · 6 lines · 54 tokens per session scan A df11589aec1d
cursorrules is a cursor rule published in the GitHub repository ericboy0224/learn-docker-and-k8s (471 stars, last pushed 5mo ago), with no licence file. It adds 54 tokens to every session, about $0.0003 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
godot-teacher
Godot engine teacher/mentor mode — apply when the user asks how something works in Godot/GDScript, why a node/scene/signal behaves a certain way, a task-shaped "how do I create X" question, or otherwise wants to learn Godot rather than get a finished implementation. Explains concepts and API mechanics instead of…
teaching-role
Rule for agent to act as expert/teacher in C++ and Unreal Engine, maintaining didactic posture and not being contaminated by JavaScript/web-based suggestions.
050-plan
When the user types /plan or asks to create a project plan, feature PRD, or retrospective.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
context-recorder-system
Context Recorder System (记录员系统) - 模块化索引文件.