Agent skill · data analytics · anthropics
metrics-review
Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.
Why this skill is useful
Provides structured analysis and actionable insights for product metrics that the AI wouldn't generate reliably on its own.
What it needs
About 11k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Metrics Review If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md. Review and analyze product metrics, identify trends, and surface actionable insights. Usage Workflow 1. Gather Metrics Data If ~~product analytics is connected: Pull key product metrics for the relevant time period Get comparison data (previous period, same period last year, targets) Pull segment breakdowns if available If no analytics tool is connected, ask the user to provide: The metrics and their values (paste a table, screenshot, or describe) Comparison data (previous period, targets) Any context on recent changes (launches, incidents, seasonality) Ask the user: What time period to review? (last week, last month, last quarter) What metrics to focus on? Or should we review the full product metrics suite? Are there specific targets or goals to compare against? Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)? 2. Organize the Metrics Structure the review using a metrics hierarchy: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down. See Product Metrics Hierarchy below for full definitions. If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding. 3. Analyze Trends For each key metric: Current value: What is the metric today? Trend: Up, down, or flat compared to previous period? Over what timeframe? vs Target: How does it compare to the goal or target? Rate of change: Is the trend accelerating or decelerating? Anomalies: Any sudden changes, spikes, or drops? Identify correlations: Do changes in one metric correlate with changes in another? Are there leading indicators that predict lagging metric changes? Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort? 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/metrics-review