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/zql0805/memforge/memforge-human-confirmgit clone --depth 1 https://github.com/zql0805/memforgeWrote 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/zql0805/memforge/memforge-human-confirm)<a href="https://agentmods.dev/rules/zql0805/memforge/memforge-human-confirm"><img src="https://agentmods.dev/badge/rules/zql0805/memforge/memforge-human-confirm.svg" alt="Measured on agentmods" 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 | $0.00784 | $0.00784 |
| Opus 5 | $0.00392 | $0.00392 |
| Sonnet 5 | $0.00157 | $0.00157 |
| Haiku 4.5 | $0.00078 | $0.00078 |
Grade A, and why
memforge-human-confirm 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 3d 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.
What it actually says
Memforge 变更前人工确认
核心原则
AI 在做任何实际改动之前,必须先向用户陈述计划并获得明确确认后才能执行。
此规则确保人类始终掌握最终决策权,避免 AI 在理解偏差的情况下执行不可逆操作。
必须确认的操作
P0 — 强制确认(绝对不可跳过)
| 操作类型 | 确认内容 |
|---|---|
| 修改代码文件 | 列出将修改的文件、改动要点、影响范围 |
| 创建新文件 | 说明文件路径、用途、与现有代码的关系 |
| 删除文件/代码 | 明确列出将删除的内容,说明删除原因 |
| 执行 Shell 命令 | 展示完整命令,说明预期效果(尤其是 npm install、数据库操作等有副作用的命令) |
| 架构或设计决策 | 阐述方案选型理由、备选方案及其权衡 |
P1 — 建议确认(复杂场景下必须确认)
| 操作类型 | 确认内容 |
|---|---|
| 重构/重命名 | 影响的文件列表、重构策略、回归风险 |
| 修改配置文件 | 配置项变更对照表、影响的环境/服务 |
| 依赖变更 | 新增/升级/删除依赖的名称、版本、安全性 |
| 数据库变更 | DDL/DML 的完整内容、对现有数据的影响 |
确认流程
第一步:分析并制定计划
AI 在理解用户需求后,先完成分析,然后输出结构化的执行计划:
## 执行计划
**目标**:<一句话描述要达成的目标>
**改动清单**:
1. `path/to/file.ts` — <改动要点>
2. `path/to/another.ts` — <改动要点>
**影响评估**:
- <哪些功能可能受影响>
- <是否需要更新测试>
**风险点**:
- <潜在风险及应对>
第二步:等待用户确认
必须明确询问用户是否同意执行,例如:
- "以上计划是否可以执行?"
- "需要调整哪些部分?"
第三步:获得确认后执行
只有在用户明确同意后(如"可以"、"执行"、"OK"、"没问题"),才开始实际执行改动。
例外场景(可跳过确认)
以下操作因无副作用,可直接执行无需确认:
- 读取文件(Read / Glob / Grep)
- 查看 lint 结果(ReadLints)
- 回答知识性问题(不涉及代码改动)
- 检索记忆(recall_memory)
- **用户明确说"直接做"/"不用问"/"自动执行"**时
违规识别
以下行为视为违规:
- 未说明计划就直接修改代码文件
- 未等用户确认就执行有副作用的 Shell 命令
- 在用户说"我想了解一下"时直接开始改代码
- 跳过风险评估直接执行高风险操作(如删除、数据库变更)
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.
- 3d ago First seen · 87 lines · 784 tokens per session scan A 40c9e267779e
memforge-human-confirm is a cursor rule published in the GitHub repository zql0805/memforge (2 stars, last pushed 2mo ago), licensed MIT. It adds 784 tokens to every session, about $0.0039 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.
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