Pm Cmd Setup Metrics

/setup-metrics -- Product Metrics Dashboard Design

Design a comprehensive metrics framework for your product or feature — from selecting the right North Star to defining alert thresholds that catch problems early.

Invocation

/setup-metrics SaaS project management tool
/setup-metrics New checkout flow we just launched
/setup-metrics             # asks what you're measuring

Workflow

Step 1: Understand What to Measure

Ask the user:

• What product or feature area are you setting up metrics for?
• What stage is it in? (pre-launch, recently launched, mature)
• What are the current business goals or OKRs?
• Do you have existing metrics? What's missing or broken?
• What analytics tools are you using? (helps tailor implementation advice)

Step 2: Define the Metrics Framework

Apply the metrics-dashboard skill:

North Star Metric:

• Identify the single metric that best captures the value your product delivers to users
• Validate against criteria: measures value delivery, is a leading indicator, is actionable
• Define the metric precisely (formula, data source, time window)

Input Metrics (3-5):

• Identify the levers that drive the North Star
• Each input metric should be directly actionable by a team
• Map the causal chain: Input → North Star → Business Outcome

Health Metrics (3-5):

• Metrics that should stay stable — if they degrade, something is wrong
• Examples: error rates, latency, support ticket volume, NPS, churn rate
• Define "healthy" ranges and degradation thresholds

Counter-Metrics (1-2):

• Metrics that could indicate you're optimizing the wrong way
• Example: if North Star is "daily active users", counter-metric is "session quality" to prevent empty engagement

Step 3: Design Alert Thresholds

For each metric:

• Yellow: Investigate — something may be off
• Red: Act immediately — page someone or escalate

Step 4: Create Dashboard Spec

## Metrics Dashboard: [Product/Feature]

**North Star**: [metric name]
**Definition**: [precise formula]
**Current value**: [if known]
**Target**: [goal]

### Input Metrics
| Metric | Definition | Owner | Target | Current |
|--------|-----------|-------|--------|---------|

### Health Metrics
| Metric | Healthy Range | Yellow Threshold | Red Threshold |
|--------|-------------|-----------------|---------------|

### Counter-Metrics
| Metric | Why It Matters | Watch For |
|--------|---------------|-----------|

### Metrics Tree
North Star: [metric]
├── Input: [metric 1] → driven by [team/action]
├── Input: [metric 2] → driven by [team/action]
├── Input: [metric 3] → driven by [team/action]
└── Counter: [metric] → watch for [degradation signal]

### Implementation Notes
- Data sources: [where each metric comes from]
- Refresh frequency: [real-time / hourly / daily]
- Tool recommendations: [based on user's stack]

### Review Cadence
- **Daily**: Glance at North Star and health metrics
- **Weekly**: Review input metrics trends, discuss in team standup
- **Monthly**: Deep dive — are inputs driving the North Star as expected?
- **Quarterly**: Reassess the metrics framework itself

Save as a markdown file to the user's workspace.

Step 5: Offer Next Steps

• "Want me to write SQL queries to compute these metrics?"
• "Should I create OKRs based on this metrics framework?"
• "Want me to build a cohort analysis to set realistic baselines?"
• "Should I set up a weekly metrics review template?"

Notes

• A good North Star is rare — most teams pick vanity metrics. Push for a metric that captures user value delivered, not just engagement
• Input metrics should be MECE (mutually exclusive, collectively exhaustive) in explaining the North Star
• If the product is pre-launch, define metrics now but note that baselines will need calibration after launch
• Counter-metrics prevent Goodhart's Law — when a metric becomes a target, it ceases to be a good metric
• Recommend starting with fewer metrics, well-instrumented, over a sprawling dashboard nobody checks

是什么

/setup-metrics -- Product Metrics Dashboard Design 用来把 数据分析师 场景里的任务输入转成可执行的流程、检查清单和交付物。

Design a product metrics dashboard with North Star metric, input metrics, health metrics, and alert thresholds

它的价值在于让 数据AI职能线 在 Claude Code、Codex、Gemini、Hermes 或 OpenClaw 中复用同一套岗位能力，而不是依赖一次性的聊天提示词。

怎么用

1. 明确当前任务目标、输入材料、约束和期望交付物，再加载 pm-cmd-setup-metrics。
2. 按 skill 文档中的流程、检查清单或工具建议执行，优先复用仓库已有规范与真实命令。
3. 把关键判断、风险、验证命令和产出路径记录到当前任务文档或交付说明中。
4. 用最小可证明的检查确认结果有效；发现缺口时回到 skill 清单补齐。

架构图

flowchart LR
  A[任务输入] --> B[加载 /setup-metrics -- Product Metrics Dashbo]
  B --> C[执行流程与检查清单]
  C --> D[生成交付物与风险记录]
  D --> E[验证结果并沉淀复盘]