Product Analytics Setup
技能 已验证 活跃How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.
To guide users in setting up and maintaining accurate, trustworthy product analytics instrumentation that supports strategic decision-making.
功能
- Detailed guidance on event taxonomy design and naming conventions
- Best practices for property design (event-level vs user-level) and type discipline
- Frameworks for schema versioning and migration patterns
- Templates and rules for designing effective funnels and cohort definitions
- Guidance on North Star metric selection and supporting metrics
- Principles for dashboard hygiene and managing instrumentation debt
使用场景
- Setting up product analytics instrumentation from scratch
- Auditing and improving an existing, untrustworthy analytics setup
- Designing instrumentation for new features
- Establishing and enforcing data quality standards for product analytics
非目标
- Analytics strategy and measurement framework selection
- Experimentation result interpretation
- Paid media analytics or platform decisions
- Specific tool setup (Mixpanel, Amplitude, etc.) beyond general principles
实践
- Data Quality
- Instrumentation Discipline
- Schema Management
安装
npx skills add rampstackco/claude-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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