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 skills add konglong87/superPM --skill pm-crossgit clone --depth 1 https://github.com/konglong87/superPMWrote 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/skills/konglong87/superpm/pm-cross)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-cross"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-cross/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/skills/konglong87/superpm/pm-cross"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-cross.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.03542 |
| Opus 5.5 | $0.00022 | $0.01417 |
| Sonnet 5.5 | $0.00011 | $0.00708 |
| Haiku 4.5 | $0.00006 | $0.00354 |
Grade A, and why
pm-cross 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 26d 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 — 482 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble
echo "🤝 跨部门协作方案制定工具已启动"
写入时边界
只讨论时不写文件,启动检查不创建目录。用户要求保存产物时,先确认当前产品、精确输出路径及已有文件的覆盖/追加选择;确认后仅在实际 Write 前创建该输出文件的父目录。完整安装可参见 skills/_shared/artifact-policy.md,单独安装本 skill 也按本段执行。
跨 Agent 交互规则
当流程要求与用户交互时:
- 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
- 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
- 一次只问一个问题。
- 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
- 不得在用户回答前生成文档、写入 docs。
- 已有 docs 文件不能替代本轮用户回答。
执行流程
步骤 1: 识别参与部门
使用 AskUserQuestion:
🏢 参与部门识别
本次项目涉及哪些部门?(可多选)
A) 产品部门(需求定义、验收) B) 研发部门(技术实现) C) 设计部门(UI/UX设计) D) 测试部门(质量保障) E) 运营部门(推广、用户反馈) F) 市场部门(品牌、宣传) G) 客服部门(用户支持) H) 法务部门(合规审查) I) 财务部门(预算审批) J) 其他部门(请手动输入)
步骤 2: 明确协作场景
使用 AskUserQuestion:
🔗 协作场景识别
主要的跨部门协作场景:
A) 需求确认与评审 B) 设计稿评审 C) 技术方案评审 D) 测试用例评审 E) 上线发布评审 F) 问题排查与解决 G) 多项组合
步骤 3: 设计协作流程
使用 AskUserQuestion:
📋 协作流程模式
选择协作流程的严格程度:
A) 正式流程(多级审批、文档完备) B) 标准流程(关键节点评审) C) 灵活流程(快速沟通、轻量文档)
继续询问:
🔄 协作依赖关系
部门间是否存在强依赖关系?
A) 串行依赖(部门A完成 → 部门B开始) B) 并行协作(多部门同时进行) C) 混合模式(部分串行、部分并行)
步骤 4: 设计沟通机制
使用 AskUserQuestion:
💬 沟通会议频率
跨部门沟通会议的频率:
A) 每日(快速同步) B) 每周(周会) C) 每迭代(迭代评审) D) 按需(有重要事项时召开)
继续询问:
📢 信息同步方式
项目信息同步的主要方式:
A) 文档协作(Notion/飞书文档) B) 群组沟通(微信群/飞书群) C) 邮件通报 D) 项目管理工具(Jira/Trello) E) 多种方式组合
步骤 5: 明确职责分工
使用 AskUserQuestion:
🎯 决策权归属
关键决策由谁负责:
A) 产品负责人(PO)决策 B) 项目经理(PM)协调决策 C) 委员会决策(多部门负责人) D) 分层决策(按决策类型划分)
步骤 6: 生成跨部门协作方案
使用 Write 工具生成 docs/04-风控管理/跨部门协作方案.md。
Subagent 并行加速(v2.0.0 新增)
利用 Agent 工具并行执行独立子任务,大幅缩短总执行时间。
可并行子任务
当步骤1-2的用户信息收集完成后,以下两个任务可以并行执行:
| 子任务 | 说明 |
|---|---|
| RACI矩阵生成 | 基于参与部门和协作场景,自动生成职责分配矩阵 |
| 沟通机制设计 | 根据部门关系和协作模式,输出沟通会议和信息同步方案 |
触发方式
在步骤6生成文档前,使用 Agent 工具激活子任务并行执行。
V1 vs V2 对比
| 维度 | V1.1.0(串行) | V2.0.0(Subagent并行) | 节省 |
|---|---|---|---|
| 职责分配 | 用户逐一回答决策权问题 | Agent并行生成RACI矩阵 | 约3轮交互 |
| 沟通设计 | 依次询问会议和信息方式 | Agent并行输出沟通方案 | 约2轮交互 |
| 总交互轮次 | 约8-10轮 | 约4-6轮 | 减少45%+ |
| 耗时估算 | 8-12分钟 | 5-7分钟 | 节省约5分钟 |
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.
- 26d ago Changed · +3 lines 338c27a430f3
- 1mo ago First seen · 479 lines · 55 tokens per session scan A 6f7bf3ca634f
pm-cross is a skill published in the GitHub repository konglong87/superPM (77 stars, last pushed 7d ago), licensed MIT. It adds 55 tokens to every session and 3,542 once invoked, about $0.0002 per session on Opus 5.5. 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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