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 5.5 | $0.00000 | $0.00412 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 11mo 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…
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).
sprint-planning
BMAD BMM Agent: sprint-planning.