metrics-dashboard

This skill acts as an analytical framework for product managers to design, audit, and implement robust product metrics dashboards. It helps translate ambiguous product goals into concrete, measurable signals. Whether you are setting up new analytics tracking for a feature launch or auditing existing dashboard efficacy, this skill provides a structured path for identifying what truly matters. It bridges the gap between high-level business strategy and operational reality by helping you distinguish between vanity metrics and actionable insights using industry-standard frameworks like the North Star Metric (NSM), AARRR, and Google HEART.

• Define core metrics hierarchies including the North Star Metric, input metrics, health guardrails, and business metrics.

• Architect dashboard layouts tailored to specific tracking frequencies: daily operational health, weekly engagement trends, and monthly business outcomes.

• Configure alert thresholds based on specific data sources and expected response times to ensure teams remain proactive.

• Provide tool-agnostic recommendations for implementation across platforms like Amplitude, Mixpanel, PostHog, Looker, Metabase, and Datadog.

• Audit existing metrics against the '4 Criteria for a Good Metric' (understandable, comparative, ratio/rate-based, and behavior-changing).

• Use in conjunction with data analysis skills to map specific user behavior to database events and visualization types.

• Input: User provides product strategy docs, existing OKRs, or specific data source documentation. If no files are provided, the skill initiates a discovery phase to determine the core value proposition.

• Output: A comprehensive, markdown-formatted dashboard specification including metric definitions, calculation methodologies, visualization types, and proactive alert strategies.

• Constraints: Focuses on behavior-changing metrics; actively discourages vanity metrics that do not influence product decision-making or iteration speed.

• Best practices: Always verify data source feasibility before finalizing alert thresholds to avoid alert fatigue. Regularly review metrics for shifts in product-market fit or business strategy pivots.