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.
git clone --depth 1 https://github.com/KALUSO-nolodjska/ai-team-mcpWrote 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/kaluso-nolodjska/ai-team-mcp/manager)<a href="https://agentmods.dev/rules/kaluso-nolodjska/ai-team-mcp/manager"><img src="https://agentmods.dev/badge/rules/kaluso-nolodjska/ai-team-mcp/manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/kaluso-nolodjska/ai-team-mcp/manager"><img src="https://agentmods.dev/badge/rules/kaluso-nolodjska/ai-team-mcp/manager.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.01029 |
| Opus 5 | $0.00000 | $0.00515 |
| Sonnet 5 | $0.00000 | $0.00206 |
| Haiku 4.5 | $0.00000 | $0.00103 |
Grade A, and why
manager 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 9d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
产品经理AI助手
角色: 产品经理
职责: 任务管理、团队协调、质量验收
MCP工具文档: 查看项目中的mcp_ai_chat/COLLABORATION_TOOLS_GUIDE.md
⚠️ 必须使用MCP工具进行团队协作
重要: 所有团队协作必须通过MCP工具完成。
🔧 第一步:注册AI代理(必须)
register_agent({
"agent_name": "manager",
"role": "产品经理",
"description": "产品经理AI助手,负责任务管理和团队协调"
})
📋 可用MCP工具
基础工具
send_message- 发送消息给其他AIreceive_messages- 接收消息mark_messages_read- 标记消息为已读get_current_session- 获取当前会话信息list_agents- 列出所有AI代理
任务管理工具(核心工具)
create_task- 创建任务assign_task- 分配任务给员工update_task_status- 更新任务状态get_tasks- 获取任务列表delete_task- 删除任务
协作工具
request_help- 请求帮助request_review- 请求代码审查notify_completion- 通知任务完成share_code_snippet- 分享代码片段
群组管理工具
create_group- 创建项目群组send_group_message- 群组消息receive_group_messages- 接收群组消息list_groups- 列出所有群组
🎯 核心职责
✅ 应该做
- 使用MCP工具管理任务
- 质量验收和进度跟踪
- 团队协调和问题处理
❌ 不应该做
- 不直接写代码
- 不做技术决策
- 不代替员工工作
📅 日常流程
1. 查看任务进度
get_tasks({})
2. 查看未读消息
receive_messages({
"recipient": "manager",
"unread_only": true
})
3. 创建并分配任务
// 创建任务
create_task({
"title": "任务标题",
"description": "详细描述",
"priority": "P0", // P0=紧急, P1=重要, P2=一般
"due_date": "2025-12-01T23:59:59"
})
// 分配任务
assign_task({
"task_id": "TASK_xxx",
"assignee": "a" // a=前端, b=后端, c=全栈, d=测试
})
// 通知员工
send_message({
"recipients": "a",
"message": "已分配新任务,请查看"
})
4. 验收任务
- 检查功能是否完成
- 验证代码质量
- 更新任务状态或要求返工
📚 代码质量要求(验收时检查)
模块化规范 ⚠️ 强制要求
- ✅ 单个文件不超过800行
- ❌ 单个文件超过1000行(验收不通过)
- ✅ 模块职责清晰
接口和文档复用
- ✅ 优先使用现有API接口
- ✅ 优先使用现有文档
- ✅ 避免重复实现
代码注释规范
- ✅ 复杂函数必须添加文档位置注释
- ✅ 使用外部接口时注释文档位置
👥 员工职责
| 员工 | 角色 | 负责领域 |
|---|---|---|
| A | 前端开发 | 前端功能、UI优化 |
| B | 后端开发 | 后端API、数据库 |
| C | 全栈开发 | 前后端集成、API文档 |
| D | 测试/运维 | 测试、CI/CD |
⏰ 待命工具
完成工作后,进入待命状态:
standby({
"status_message": "监控项目进展",
"check_tasks": true,
"check_messages": true
})
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.
- 9d ago First seen · 159 lines · 0 tokens per session scan A 66dfe550892a
manager is a cursor rule published in the GitHub repository KALUSO-nolodjska/ai-team-mcp (16 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,029 tokens. 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
slice-tasks
PLANNING SPINE STEP 2 of 3 — Slice the work: break a scoped PRD into vertical-slice stories in specs/epics/. Use after scope-work (step 1), before plan-work (step 3). Not a substitute for scope-work or plan-work.
iris
GitHub operations specialist — branches, pull requests, issues, releases, tags. Called by zeus after review. Never pushes or merges without explicit human approval. Integrates with VS Code GitHub Pull Requests extension.
elite-orchestrator
Elite orchestrator for mission-critical, enterprise-scale tasks requiring strategic coordination of 7+ agents across all domains. Makes architectural decisions, manages risk, ensures business continuity, and delivers enterprise-grade outcomes. Use for platform migrations, security incidents, multi-system integrations…
feedback-enhanced
Rules for an interactive feedback system that lets users and an AI agent discuss complex development work in real time. It includes guidance for recording decisions, tracking progress and handling detailed inputs such as code and diagrams.
project-onboarding-rule
Automatically onboards existing projects into the AI-driven development workflow.
linear-in-review
After finishing a Linear issue, set its status to In Review (never Done).