Analyze product metrics trends and anomalies into a decision-ready scorecard.
npx skills add https://github.com/ngochuy13/intern-dev --skill metrics-review-ngochuy13
Please help me install this Agent Skill. Skill: metrics-review Source: https://github.com/ngochuy13/intern-dev/tree/main/skills/metrics-review Command: npx skills add https://github.com/ngochuy13/intern-dev --skill metrics-review-ngochuy13
This Skill helps you interpret product metrics so you can quickly understand what changed, why it may have changed, and what to do next.
Run /metrics-review for the last quarter to generate a metrics review summary, scorecard, trend explanations, bright spots, areas of concern, and recommended next actions.
High-intent search queries and answers about installing and using this skill.
To turn raw product metrics into a decision-ready scorecard, you analyze trends, anomalies, and target deviations. This process organizes numbers by a North Star/L1/L2 hierarchy to output a clear scorecard with status indicators, insights, and recommended actions.
The best way to investigate metric spikes or drops is by applying segmentation and correlation analysis to your product metrics. This identifies anomaly drivers and deviations from targets, translating the findings into a structured investigation plan with follow-up questions and recommended experiments.
You structure weekly or quarterly metrics check-ins by comparing current performance against previous periods and OKR targets. Organizing metrics into a North Star/L1/L2 hierarchy allows you to quickly generate a summary, identify bright spots and areas of concern, and track goal progress.
Analyzing segment drivers and correlations requires a predefined North Star/L1/L2 metric hierarchy. Without this structured framework, the metrics review cannot accurately map correlations, identify segment deviations from targets, or output a focused scorecard with actionable insights.
A limitation when comparing product performance against goals is that the analysis outputs caveats alongside recommended actions. The scorecard relies strictly on the provided metric hierarchy and segmentation data, meaning incomplete input datasets will limit the accuracy of trend explanations and anomaly investigations.
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