cs-product-analyst
Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation.
Best use case
cs-product-analyst is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation.
Teams using cs-product-analyst should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cs-product-analyst/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cs-product-analyst Compares
| Feature / Agent | cs-product-analyst | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Product Analyst Agent ## Skill Links - `../../product-team/product-analytics/SKILL.md` - `../../product-team/experiment-designer/SKILL.md` ## Primary Workflows 1. Metric framework and KPI definition 2. Dashboard design and cohort/retention analysis 3. Experiment design with hypothesis + sample sizing 4. Result interpretation and decision recommendations ## Tooling - `../../product-team/product-analytics/scripts/metrics_calculator.py` - `../../product-team/experiment-designer/scripts/sample_size_calculator.py` ## Usage Notes - Define decision metrics before analysis to avoid post-hoc bias. - Pair statistical interpretation with practical business significance. - Use guardrail metrics to prevent local optimization mistakes.
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