g-system

A conversation rule for handling complex questions that lack important details. It asks for context such as the role, background, goal, desired output, and ways to verify the answer.

In plain words
What is it for?
Use it when a user provides an incomplete complex request, such as planning a system migration, and needs guidance on what information to add.
Why use it?
It prevents the agent from guessing when a request is underspecified. The result is a more focused answer with clearer constraints and risk considerations.

Cursor rule

Install

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.

agentmods
npx agentmods add rules/xyzbit/ai-coding/g-system
Clone the repo
git clone --depth 1 https://github.com/xyzbit/AI-Coding
Per session 280 This file is loaded in full into every session.
When invoked 280 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00280 $0.00280
Opus 5 $0.00140 $0.00140
Sonnet 5 $0.00056 $0.00056
Haiku 4.5 $0.00028 $0.00028

Measured 2d ago against content hash 9debbabf5863, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

g-system 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.

cursor/rules/g-system.mdc · 26 lines

What it actually says

Chat 要求

如果判断是一个复杂问题,用户提供信息不全,则引导用户对提出的问题进行补充, 问题的内容参考如下结构+示例 Prompt 结构:

  • 角色设定:明确 AI 扮演的身份(如技术专家、产品经理、法律顾问)
  • 问题背景:提供必要的上下文、数据或场景假设
  • 任务目标:清晰描述需要完成的具体任务(分析、设计、优化等)
  • 输出要求:指定格式、深度、风格、约束条件
  • 验证维度:要求包含风险分析、数据支持或替代方案

示例:

*“作为资深 DevOps 工程师,某公司计划将单体应用迁移至 Kubernetes 集群。已知现有系统日均请求量 100 万次,数据库使用 MySQL。请设计迁移方案,需包含:

迁移阶段划分(开发、测试、生产)
关键技术点(服务发现、负载均衡、数据库迁移策略)
风险控制措施
要求用流程图 + 文字说明,篇幅控制在 500-800 字,引用 Kubernetes 官方文档最佳实践。”*
Changes

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.

  1. 2d ago First seen · 26 lines · 280 tokens per session scan A 9debbabf5863

Subscribe to this mod's changes

g-system is a cursor rule published in the GitHub repository xyzbit/AI-Coding (21 stars, last pushed 10mo ago), licensed Apache-2.0. It adds 280 tokens to every session, about $0.0014 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.