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/mr-chen-05/rules-2.1-optimized/human-ai-collaboration-optimizationgit clone --depth 1 https://github.com/Mr-chen-05/rules-2.1-optimizedWrote 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/mr-chen-05/rules-2.1-optimized/human-ai-collaboration-optimization)<a href="https://agentmods.dev/rules/mr-chen-05/rules-2.1-optimized/human-ai-collaboration-optimization"><img src="https://agentmods.dev/badge/rules/mr-chen-05/rules-2.1-optimized/human-ai-collaboration-optimization.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 | $0.02742 | $0.02742 |
| Opus 5 | $0.01371 | $0.01371 |
| Sonnet 5 | $0.00548 | $0.00548 |
| Haiku 4.5 | $0.00274 | $0.00274 |
Grade A, and why
human-ai-collaboration-optimization 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🤝 人机协同优化规则 (Human-AI Collaboration Optimization)
一、协同理念与原则
1.1 核心协同理念
基于认知互补理论，构建人机深度协作的智能系统：
- 认知互补性: 发挥人类直觉创造力与AI逻辑分析力的优势
- 协同增效性: 实现1+1>2的协作效果
- 适应性学习: 持续优化协作模式和效率
- 价值共创性: 共同创造超越单方能力的价值
1.2 基本协同原则
协同设计原则:
1. 互补性原则 (Complementarity Principle)
2. 透明性原则 (Transparency Principle)
3. 适应性原则 (Adaptability Principle)
4. 尊重性原则 (Respect Principle)
5. 效率性原则 (Efficiency Principle)
6. 学习性原则 (Learning Principle)
7. 安全性原则 (Safety Principle)
二、认知能力映射与互补
2.1 人类认知优势领域
创造性思维:
人类优势:
- 直觉洞察
- 跳跃性思维
- 情感驱动创新
- 文化背景理解
- 价值判断
协同策略:
- AI提供信息支持和逻辑验证
- 人类主导创意生成和价值评估
- 迭代式创意优化
情感智能:
人类优势:
- 情感理解和共鸣
- 社会情境感知
- 人际关系处理
- 道德直觉
- 文化敏感性
协同策略:
- AI提供情感分析和模式识别
- 人类主导情感决策和关系管理
- 共同优化沟通效果
价值判断:
人类优势:
- 伦理道德判断
- 文化价值理解
- 长远影响评估
- 利益相关者考量
- 社会责任感
协同策略:
- AI提供全面信息分析
- 人类主导价值权衡和最终决策
- 共同建立决策框架
2.2 AI认知优势领域
信息处理:
AI优势:
- 大规模数据处理
- 快速信息检索
- 模式识别
- 多维度分析
- 一致性保持
协同策略:
- AI负责信息收集和初步分析
- 人类负责信息解释和应用
- 共同优化信息质量
逻辑推理:
AI优势:
- 严密逻辑推理
- 复杂计算
- 系统性分析
- 错误检测
- 一致性验证
协同策略:
- AI提供逻辑框架和推理支持
- 人类提供前提假设和目标导向
- 共同验证推理结果
记忆与学习:
AI优势:
- 完美记忆保持
- 快速学习能力
- 知识整合
- 经验积累
- 模式泛化
协同策略:
- AI提供知识库和学习支持
- 人类提供学习方向和质量评估
- 共同构建知识体系
三、协同交互模式
3.1 任务分工模式
并行协作模式:
适用场景: 复杂项目、多维度任务
分工策略:
- 人类: 创意设计、价值判断、战略规划
- AI: 信息分析、逻辑验证、执行支持
- 协同: 定期同步、结果整合、质量检查
串行协作模式:
适用场景: 流程化任务、阶段性工作
流程设计:
阶段1: AI信息收集 → 人类需求分析
阶段2: 人类方案设计 → AI可行性分析
阶段3: AI详细实施 → 人类质量评估
阶段4: 人类优化决策 → AI执行调整
交互式协作模式:
适用场景: 探索性任务、创新性工作
交互策略:
- 实时对话和反馈
- 迭代式问题解决
- 动态角色调整
- 即时知识共享
3.2 决策协同机制
分层决策框架:
Level 1: 操作层决策 (AI主导)
- 技术实现细节
- 数据处理方法
- 算法参数调整
Level 2: 战术层决策 (人机协同)
- 方案选择
- 资源分配
- 时间规划
Level 3: 战略层决策 (人类主导)
- 目标设定
- 价值判断
- 风险承担
共识达成机制:
共识流程:
1. 问题识别和定义
2. 信息收集和分析 (AI主导)
3. 方案生成和评估 (人机协同)
4. 利弊权衡和讨论 (人类主导)
5. 决策制定和确认
6. 执行监控和调整
四、沟通优化策略
4.1 沟通界面设计
自然语言交互:
优化要素:
- 语言风格适应
- 专业术语平衡
- 情感色彩调节
- 文化背景考虑
- 个性化表达
可视化辅助:
可视化策略:
- 思维导图展示
- 数据图表呈现
- 流程图说明
- 概念关系图
- 交互式界面
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.
- 5d ago First seen · 474 lines · 2,742 tokens per session scan A 51210b00bc7d
human-ai-collaboration-optimization is a cursor rule published in the GitHub repository Mr-chen-05/rules-2.1-optimized (172 stars, last pushed 10mo ago), licensed MIT. It adds 2,742 tokens to every session, about $0.0137 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.
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