监控产品指标、检测异常并跟踪 KPI。当用户需要设置监控、跟踪产品指标、分析数据趋势、创建仪表板、检测异常，或说"跟踪 X 指标"、"Y 表现如何"、"设置监控"时使用。即使没有明确说"数据监控"，当用户正在处理指标、分析或性能跟踪时也应激活。

Data Monitoring

Track metrics that matter for product success.

What This Skill Does

Sets up and monitors product metrics including core KPIs, user behavior metrics, and business indicators. Detects anomalies, tracks trends, and generates alerts. The output provides the data foundation for impact analysis and iteration planning.

When to Use

Activate this skill when:

User wants to track product metrics or KPIs
Phrases like "monitor X", "track Y metric", "how is it performing"
Setting up dashboards or alerting
Analyzing trends or detecting anomalies
User says "set up monitoring" or "create dashboard"

How It Works

The monitoring process ensures comprehensive metric tracking:

Define metrics - Identify what to measure based on product goals
Set up tracking - Configure data collection and storage
Establish baselines - Determine normal ranges and thresholds
Configure alerts - Set up anomaly detection and notifications
Monitor trends - Track changes over time
Generate insights - Identify patterns and issues

Input Parameters

Parameter	Type	Required	Description
`metric_type`	string	Yes	Type of metric (e.g., "engagement", "revenue", "technical")
`kpi_name`	string	Yes	Specific KPI to monitor
`threshold`	object	No	Alert thresholds (e.g., `{"min": 100, "max": 1000}`)
`frequency`	string	No	Check frequency (default: "daily")

Metric Categories

Category	Example Metrics	Use For
`engagement`	DAU, WAU, MAU, session length	User engagement
`acquisition`	Signups, conversion rate, CAC	Growth tracking
`retention`	Day-7, Day-30, churn rate	User retention
`revenue`	MRR, ARPU, LTV	Business health
`technical`	Error rate, latency, uptime	System health
`feature`	Feature adoption, usage frequency	Feature success

Output Structure

The skill generates monitoring outputs:

Alert notifications - Real-time alerts when thresholds breached
Trend reports - Regular summaries of metric performance
Dashboard specifications - Dashboard configuration recommendations

Alert Format

{
  "alert": {
    "id": "alert-uuid",
    "timestamp": "2026-03-12T10:30:00Z",
    "severity": "warning",
    "metric": "daily_active_users",
    "current_value": 850,
    "threshold": {"min": 1000},
    "change": "-15%",
    "message": "DAU dropped below threshold",
    "suggested_actions": [
      { "action": "Check if deployment caused issues", "confidence": "low", "basis": "Correlation with recent deployment" }
    ]
  }
}

Trend Report Format

{
  "report": {
    "period": "2026-03-06 to 2026-03-12",
    "metrics": [
      {
        "name": "daily_active_users",
        "current": 1050,
        "previous": 1000,
        "change": "+5%",
        "trend": "up",
        "status": "healthy",
        "data_source": "internal analytics",
        "benchmark": { "value": "N/A", "confidence": "unknown", "note": "No industry benchmark found for this metric" }
      },
      {
        "name": "conversion_rate",
        "current": "3.2%",
        "previous": "3.5%",
        "change": "-8.6%",
        "trend": "down",
        "status": "warning"
      }
    ],
    "anomalies": [
      {
        "metric": "conversion_rate",
        "detected_at": "2026-03-10",
        "description": "Unusual drop after deployment"
      }
    ],
    "recommendations": [
      "Investigate conversion rate drop",
      "Consider A/B testing checkout flow"
    ]
  }
}

Anomaly Detection

Type	Description	Detection Method
Spike	Sudden increase	Value > 3σ from mean
Drop	Sudden decrease	Value < 3σ from mean
Trend	Gradual change	Linear regression slope
Pattern	Repeating pattern	Time series analysis

Anti-Hallucination Rules

本 Skill 必须遵守 skills/shared/anti-hallucination-rules.md 中的约束：

搜索先行: 引用行业基准数据前必须先搜索验证
来源必注: 每个指标基准值、行业对比数据必须有来源
未知可接受: 无法获取的真实指标标注 "Unknown"，禁止编造基线数据
置信度: 关键基准数据标注 confidence (high/medium/low)
事实与推断分离: 区分实际监控数据和推断的异常原因

Quality Standards

Before delivering, the monitoring setup should:

Define clear metrics with business relevance
Set appropriate thresholds based on real historical data or industry benchmarks
ALL industry benchmarks cited have source URLs
ALL suggested root causes have confidence rating
Facts (actual metric values) and inferences (suspected causes) are clearly distinguished
Include actionable alert messages
Provide context for metric interpretation
Specify data sources and collection methods

Collaboration Modes

Mode	How It Works
`autopilot`	Sets up monitoring with recommended thresholds
`copilot`	Proposes metrics and thresholds, confirms with you
`manual`	Provides monitoring templates, you configure

Context Integration

Reads:

context/prd/*.md - Success metrics defined in PRDs

Writes:

context/metrics.json - Current metric values and trends

Read By:

impact-analysis - Uses metric data for post-release analysis
iteration-planning - References trends for planning decisions

Example Usage

User: "Monitor our daily active users"
→ Sets up DAU monitoring with alerts

User: "Create a dashboard for key metrics"
→ Generates dashboard specification with core KPIs

User: "Why did conversion rate drop yesterday?"
→ Analyzes anomaly, provides root cause suggestions

data-monitoring