pm-data

pm-data is a skill for Claude Code from konglong87/superPM. It costs 50 tokens per session (3,187 once invoked), scanned A, original, MIT.

A workflow for defining product metrics, tracking events, monitoring results, and planning data analysis.

In plain words
What is it for?
Use it to design an overall measurement system, core product metrics, feature tracking, conversion funnels, user groups, or trend analysis.
Why use it?
It helps turn product goals into measurable signals and checks for the planning documents needed to define those signals. It is unnecessary when the metrics are already settled.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the AskUserQuestion tool.

Part of the super-pm plugin — 57 skills, 1 hook shipped together

Good fit Use it to design an overall measurement system, core product metrics, feature tracking, conversion funnels, user groups, or trend analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/konglong87/superpm/pm-data
Install

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.

Any agent
npx skills add konglong87/superPM --skill pm-data
Clone the repo
git clone --depth 1 https://github.com/konglong87/superPM

Made for: Claude Code.

Or install super-pm, the plugin that ships this one along with the rest of its 57 skills, 1 hook.

Wrote 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.

agentmods badge for pm-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-data/github.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-data)
Your own site
<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.

agentmods 80×15 button for pm-data

Your own site · 80×15
<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>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,187 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 24d ago against content hash d38a35298264, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/02-solution-design/pm-data/SKILL.md · 374 lines

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:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 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。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 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

Read the full file on GitHub · 374 lines

Files

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.

Changes

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.

  1. 24d ago Changed · +4 lines d38a35298264
  2. 1mo ago First seen · 370 lines · 50 tokens per session scan A 7582a55236f6

Subscribe to this mod's changes

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.