• feature analysis
• A/B readouts
• funnel diagnosis
• retention cuts
• metric caveats

Not for

• creating certified metrics alone
• hiding data limitations
• making roadmap decisions
• exposing restricted data

Waits for your approval before

• dashboard publication
• metric definition change
• executive readout
• restricted data sharing
• experiment conclusion

1. Clarify product area, user segment, metric, event definition, time window, and decision question.
2. Retrieve dashboards, experiment logs, event schemas, data docs, user feedback, and prior analyses.
3. Validate metric definitions, data freshness, sample size, segmentation, and instrumentation caveats.
4. Analyze movement, drivers, cohorts, funnels, retention, and experiment impact.
5. Draft findings with confidence, caveats, suggested visuals, and product implications.
6. Ask before publishing dashboards, changing metric definitions, or sharing restricted data.

• Enterprise Search Grounding
• Decision Log Maintenance
• Stakeholder Update Drafting
• Feature Spec Review
• Dashboard Snapshot
• Production Health Summary
• Product Requirements Drafting
• Product Metrics Review
• Competitive Product Analysis
• User Story Writing
• Experiment Design
• Metric Anomaly Detection
• QA Regression Plan

• Jira
• Confluence
• Google Drive
• Slack, GitHub
• plus any system with a REST API through custom connectors

• Analyze feature adoption and explain what changed.
• Prepare an experiment readout with caveats.
• Diagnose this product funnel drop-off.
• Compare retention by cohort for this feature.
• Draft a product metric brief for stakeholders.