By tonone-ai
Define product metrics and KPIs with complete specifications including formulas, data sources, segmentation, SQL/event tracking specs, and success thresholds for any product area or feature
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npx claudepluginhub tonone-ai/tonone --plugin lens-metricsProduct analyst — metrics frameworks, funnel analysis, OKRs, A/B test design, and retention analysis
Metrics, experimentation, and data-informed product decisions.
Data & analytics skills: Metrics Framework, SQL Query Explainer, Dashboard Brief, Cohort Analysis, Data Pipeline Spec, Chart Data Extractor, A/B Test Readout, Metric Tree Builder, Data Quality Audit. Build North Star metric trees, explain and optimise SQL, spec dashboards, read out A/B test results with significance and guardrails, and audit datasets for quality before you trust them.
SaaS business metrics and marketing analytics. CAC, LTV, MRR/ARR analysis, conversion funnels, ad spend optimization, and unit economics modeling.
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Editorial "Business Analyst" bundle for Claude Code from Agentic Awesome Skills.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".