intercept-discipline

A set of rules for deciding when an AI product may warn or refuse a request. It requires human approval before adding an interception and ranks safer options first.

In plain words
What is it for?
Use it when designing prompts, review steps, warnings, refusal rules, or safety breakers. It helps document false-positive risks and explain why weaker controls are insufficient.
Why use it?
Automatic blocking can reject valid requests and make the product harder to use. These rules help limit blocking to clear contract, safety, or capability problems.

Cursor rule for Cursor

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/lawofall/agentcore/intercept-discipline
Clone the repo
git clone --depth 1 https://github.com/Lawofall/AgentCore

Made for: Cursor.

Per session 916 This file is loaded in full into every session.
When invoked 916 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 $0.00916 $0.00916
Opus 5 $0.00458 $0.00458
Sonnet 5 $0.00183 $0.00183
Haiku 4.5 $0.00092 $0.00092

Measured 2d ago against content hash 04b5188f08e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

intercept-discipline 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/intercept-discipline.mdc · 36 lines

What it actually says

拦截纪律(硬闸 · 软闸)

产品 AI「不听话」时,禁止默认提案加硬拒 / 软警告 / 扫自由文。拦截极易误伤;软闸也不免费(累计只读软提醒已撤,净负)。

已定案清单与否决表 → 编排器 · 失败与否决;收口姿势 A → 执行引擎 · 可用性诚实性;熔断诚实边界 → 安全权限与治理。本文只留 How(阶梯 / 白名单 / 提案格式)。

阶梯(必须按序;禁止跳级)

  1. 提示词 / Skill / 结构字段(playbook、deliverable;禁借机加已删 completion_criteria kind 的替代启发式 → 编排器 · 交付验收)。提示词怎么改 → prompt-design.mdc
  2. 观测 + 人审(日志、审批卡、escalate)——先看见再谈拦
  3. 一次性软提示(可忽略、不累计、不改成功路径、不扫用户长文猜意图)
  4. 硬拒——仅同时满足:条件结构化可证明;误伤面可枚举且可接受;不拦会破契约 / 安全底线 / 明确能力缺失

硬闸白名单(仅此类可提案,仍须人确认)

  • 结构契约:schema / form / 写盘 scope、合同字段(禁替代启发式)
  • 能力缺失:无执行环境却要外环验证 / 长驻就绪等(表述跟工具/契约)
  • 灾难安全:熔断级毁灭性动作、敏感读;文案须诚实「启发式兜底,并非完整拦截」。fuse 已覆盖形 → breaker DENY(禁再提可批可跑 / 仅改文案当终案)→ 安全 · 熔断

默认否决(提了也先当否决项写明)

  • 用硬/软闸「优化模型服从度」「防吹牛」「质量启发式」
  • 意图分类器:扫用户原文 / task·角色自由文猜意图再分叉(短允许表点名刷新除外)
  • 累计计数软提醒、与硬闸同条件「先软再硬」叠床架屋(成篇审计等已有意递进除外)
  • 扩大收口/书目/完成话术正则面冒充「近零误报」;禁借「漏拦」再扩面 → 执行引擎
  • 边删 kind 边加新启发式完成硬闸;把「验码绿」等伪装成新 kind / 领域 kind 扩表

提建议时的强制格式

若仍要提拦截:单列 误伤面为何阶梯 1–2 不够软是否可能净负;标「需人确认」。未写这三项 = 无效提案。与 dev-process.mdc 补丁绊线对齐:拦截类补丁触绊线 → 停、提根因重设计,不直接落地。

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 · 36 lines · 916 tokens per session scan A 04b5188f08e7

Subscribe to this mod's changes

intercept-discipline is a cursor rule published in the GitHub repository Lawofall/AgentCore (78 stars, last pushed 3d ago), licensed MIT. It adds 916 tokens to every session, about $0.0046 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.