product-metrics-design

product-metrics-design is a skill for Claude Code from ryanzhao1011/workframe. It costs 37 tokens per session (2,278 once invoked), scanned A, original, MIT.

A method for designing a product measurement system using frameworks such as AARRR, HEART, North Star, and OKR. It organizes measurements into a main outcome, supporting drivers, and guardrails that prevent harmful trade-offs.

In plain words
What is it for?
Use it to define product metrics, choose a measurement framework, set targets, design a three-level metric system, and plan monitoring.
Why use it?
It helps teams choose relevant measurements instead of collecting disconnected numbers. It also clarifies definitions, targets, owners, frequency, and data sources.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the core plugin — 37 skills, 4 agents, 11 hooks shipped together

Good fit Use it to define product metrics, choose a measurement framework, set targets, design a three-level metric system, and plan monitoring.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ryanzhao1011/workframe/product-metrics-design
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 ryanzhao1011/workframe --skill product-metrics-design
Clone the repo
git clone --depth 1 https://github.com/ryanzhao1011/workframe

Made for: Claude Code.

Or install core, the plugin that ships this one along with the rest of its 37 skills, 4 agents, 11 hooks.

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 product-metrics-design

README.md
[![agentmods](https://agentmods.dev/badge/skills/ryanzhao1011/workframe/product-metrics-design/github.svg)](https://agentmods.dev/skills/ryanzhao1011/workframe/product-metrics-design)
Your own site
<a href="https://agentmods.dev/skills/ryanzhao1011/workframe/product-metrics-design"><img src="https://agentmods.dev/badge/skills/ryanzhao1011/workframe/product-metrics-design/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 product-metrics-design

Your own site · 80×15
<a href="https://agentmods.dev/skills/ryanzhao1011/workframe/product-metrics-design"><img src="https://agentmods.dev/badge/skills/ryanzhao1011/workframe/product-metrics-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,278 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.
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 $0.00037 $0.02278
Opus 5 $0.00018 $0.01139
Sonnet 5 $0.00007 $0.00456
Haiku 4.5 $0.00004 $0.00228

Measured 10d ago against content hash 4c5c2d77b737, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

product-metrics-design 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 10d 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.

plugins/core/skills/product-metrics-design/SKILL.md · 222 lines

How it starts

The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.

产品度量设计技能

框架选择决策树

通过 3 个问题引导选择最合适的度量框架:

Q1:当前优化目标是什么?

目标类型 推荐框架
用户获取 / 留存 / 变现 AARRR
用户体验 / 功能质量 HEART
全团队对齐 / 长期战略 North Star
季度目标管理 OKR

Q2:团队阶段?

阶段 推荐框架
早期(<10 人) AARRR 或 North Star
成长期(10-50 人) North Star + 护栏指标
规模化(50+ 人) OKR 体系

Q3:度量粒度需求?

粒度 推荐框架
全局产品表现 AARRR / North Star
具体功能表现 HEART
跨团队目标对齐 OKR

可组合使用:例如 North Star(全局) + HEART(功能级) + OKR(目标管理)。

三层指标体系模板

第一层:North Star 指标

## North Star 指标

- **指标名称**:{名称}
- **精确定义**:{计算公式/统计口径}
- **业务游戏分类**:增长型 / 参与型 / 货币化型
- **当前基线**:{当前值}
- **目标值**:{目标值}({时间窗口}内)
- **观察频率**:{日/周/月}
- **数据来源**:{埋点/数据库/第三方}

第二层:输入 / 驱动指标(领先指标)

直接驱动 North Star 指标的可操作指标:

## 输入指标

| 指标名 | 类型 | 当前值 | 目标值 | 观察频率 | 数据来源 | 负责角色 |
|--------|------|--------|--------|---------|---------|---------|
| {指标1} | 领先 | {值} | {值} | {频率} | {来源} | {角色} |
| {指标2} | 领先 | {值} | {值} | {频率} | {来源} | {角色} |
| {指标3} | 滞后 | {值} | {值} | {频率} | {来源} | {角色} |

领先指标:可主动干预、先于结果变化的指标(如日活用户数、任务启动率) 滞后指标:反映最终结果、滞后于行动的指标(如月收入、流失率)

第三层:护栏指标

防止优化主指标时损害其他维度的防护指标:

## 护栏指标

| 指标名 | 护栏阈值 | 说明 |
|--------|---------|------|
| {指标1} | 不低于 {X} | 防止优化 A 时损害 B |
| {指标2} | 不高于 {Y} | 防止过度优化导致的副作用 |

各框架展开模板

AARRR 模板

## AARRR 漏斗指标

### Acquisition(获取)
| 指标 | 定义 | 目标值 |
|------|------|--------|
| 渠道来源分布 | 各渠道注册用户占比 | |
| 获客成本(CAC) | 营销总支出 / 新用户数 | |
| 渠道转化率 | 访问→注册转化 | |

### Activation(激活)
| 指标 | 定义 | 目标值 |
|------|------|--------|
| 注册→首次核心操作完成率 | 完成首次核心操作的新用户占比 | |
| 激活时间 | 注册到首次核心操作的平均时长 | |
| Aha 时刻到达率 | 达到核心价值感知点的用户占比 | |

### Retention(留存)
| 指标 | 定义 | 目标值 |
|------|------|--------|
| 7 日留存率 | 第 7 天仍活跃的用户占比 | |
| 30 日留存率 | 第 30 天仍活跃的用户占比 | |
| 流失预警 | 连续 N 天未使用的用户数 | |

### Revenue(收入)
| 指标 | 定义 | 目标值 |
|------|------|--------|
| MRR | 月度经常性收入 | |
| ARPU | 每用户平均收入 | |
| 付费转化率 | 免费→付费用户占比 | |
| 套餐升级率 | 低档→高档套餐转化 | |

### Referral(推荐)
| 指标 | 定义 | 目标值 |
|------|------|--------|
| 邀请率 | 发起邀请的用户占比 | |
| 邀请转化率 | 受邀→注册转化 | |
| 病毒系数(K) | 每个用户带来的新用户数 | |

Read the full file on GitHub · 222 lines

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. 10d ago First seen · 222 lines · 37 tokens per session scan A 4c5c2d77b737

Subscribe to this mod's changes

product-metrics-design is a skill published in the GitHub repository ryanzhao1011/workframe (4 stars, last pushed 23d ago), licensed MIT. It adds 37 tokens to every session and 2,278 once invoked, about $0.0002 per session on Opus 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 2026-08-31.

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