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-datagit 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-data)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-data"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-data/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-data"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-data.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 | $0.00050 | $0.03187 |
| Opus 5.5 | $0.00020 | $0.01275 |
| Sonnet 5.5 | $0.00010 | $0.00637 |
| Haiku 4.5 | $0.00005 | $0.00319 |
Grade A, and why
pm-data 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 24d 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
# 检查方案设计目录
# 检查前置文档
echo "📊 正在检查前置文档..."
if [ -f "docs/02-方案设计/PRD产品需求文档.md" ]; then
echo "✅ PRD文档 - 已找到"
else
echo "⏳ PRD文档 - 未找到"
fi
if [ -f "docs/01-需求调研/MVP方案.md" ]; then
echo "✅ MVP方案 - 已找到"
else
echo "⏳ MVP方案 - 未找到"
fi
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: 确定数据指标范围
使用 AskUserQuestion 询问:
您需要设计哪方面的数据指标?
A) 整体数据指标体系(北极星指标+关键指标+过程指标) B) 产品核心指标(如DAU、GMV、留存率) C) 功能埋点方案(具体功能的数据采集) D) 业务分析指标(如转化漏斗、用户分群) E) 其他(请手动输入)
💡 提示:
- 产品规划阶段 → 推荐整体数据指标体系
- 功能开发阶段 → 推荐功能埋点方案
- 产品优化阶段 → 推荐业务分析指标
记录到变量 DATA_SCOPE
步骤 2: 读取前置数据
根据指标范围读取相应文档:
必需文档:
- PRD产品需求文档(如果存在)
- MVP方案(如果存在)
可选文档:
- 需求调研报告(业务目标)
- 优先级报告
步骤 3: 定义北极星指标
使用 AskUserQuestion 引导:
🎯 北极星指标定义
基于产品目标,推荐的北极星指标:
选项1:{指标名称} - {定义} 选项2:{指标名称} - {定义}
您倾向于选择哪个?
A) 选择选项1 B) 选择选项2 C) 我有其他想法(请手动输入)
常见北极星指标参考:
| 产品类型 | 北极星指标 |
|---|---|
| 电商 | GMV(成交总额) |
| 内容 | 日活跃用户数 |
| 社交 | 用户互动次数 |
| 工具 | 完成任务数 |
| 教育 | 完课率 |
步骤 3.5: GSM 目标→信号→指标推导(v2.5新增)
在确定北极星指标后,使用 GSM 方法推导关键指标,确保指标定义从业务目标出发,而非直接推荐。
GSM 三步法:
G - Goal(业务目标)
北极星指标对应的业务目标是什么?
使用 AskUserQuestion 引导:
🎯 GSM 推导 - 业务目标
北极星指标对应的业务目标是什么?
A) 提升用户活跃度(DAU/MAU增长) B) 提升用户留存(留存率增长) C) 提升付费转化(付费率/ARPU增长) D) 提升用户传播(K因子/推荐率增长) E) 其他(请手动输入)
S - Signal(成功信号)
用户达成目标时的可观察行为是什么?
📶 GSM 推导 - 成功信号
用户达成目标时的可观察行为是什么?
A) 用户每天打开 App 3 次以上(活跃信号) B) 用户在 7 天内完成首次付费(转化信号) C) 用户连续 30 天有使用行为(留存信号) D) 用户主动邀请好友使用(传播信号) E) 其他(请手动输入)
M - Metric(量化指标)
从信号推导出 3-5 个可量化指标。
GSM 推导表示例:
| 目标 (Goal) | 信号 (Signal) | 指标 (Metric) |
|---|---|---|
| 提升用户活跃度 | 用户每天打开 App 3 次以上 | DAU、人均启动次数 |
| 提升付费转化 | 用户在 7 天内完成首次付费 | 7日付费转化率、首单时间 |
| 提升用户留存 | 用户连续 30 天有使用行为 | 30日留存率、月活跃天数 |
| 提升用户传播 | 用户主动邀请好友使用 | K因子、邀请转化率 |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 24d ago Changed · +4 lines d38a35298264
- 1mo ago First seen · 370 lines · 50 tokens per session scan A 7582a55236f6
pm-data is a skill published in the GitHub repository konglong87/superPM (76 stars, last pushed 5d ago), licensed MIT. It adds 50 tokens to every session and 3,187 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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