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 ryanzhao1011/workframe --skill product-metrics-designgit clone --depth 1 https://github.com/ryanzhao1011/workframeWrote 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/ryanzhao1011/workframe/product-metrics-design)<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.
<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>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 | $0.00037 | $0.02278 |
| Opus 5 | $0.00018 | $0.01139 |
| Sonnet 5 | $0.00007 | $0.00456 |
| Haiku 4.5 | $0.00004 | $0.00228 |
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.
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 | |
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.
- 10d ago First seen · 222 lines · 37 tokens per session scan A 4c5c2d77b737
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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