Cs Product Analyst
Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation. Use when a product question needs numbers — e.g., defining activation/retention KPIs and a dashboard spec for a new feature, or sizing an A/B test and judging whether the result is significant enough to ship.
- Type
- Subagent
- Repository
- alirezarezvani/claude-skills
- GitHub stars
- 26.6k
- License
- MIT
- Repo last updated
- Aug 30, 2026
- Source file
- agents/product/cs-product-analyst.md
- Model
- sonnet
What Cs Product Analyst is
Cs Product Analyst is a subagent published in the alirezarezvani/claude-skills repository on GitHub, which has about 26.6k stars. The repository describes itself as: “380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8 more coding agents — engineering, marketing, product, compliance, C-level advisory, research, business operations, commercial & finance, and your daily productivity skills.”
A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.
Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to Cs Product Analyst and get back a compact result.
It is set up to use these tools: Read, Write, Bash, Grep, Glob. Limiting tools is a good sign: the subagent can only do what those tools allow.
How to install Cs Product Analyst
Claude Code
- Download cs-product-analyst.md from the repository.
- Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control.
- Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.
Claude Cowork
- Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent.
- Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.
New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.
Inside the source file
An excerpt from agents/product/cs-product-analyst.md, shared under the repository's MIT license. Read the full file on GitHub.
Purpose
The cs-product-analyst agent turns product questions into measurable answers. It orchestrates the product-analytics and experiment-designer skills to define metric frameworks, compute retention/cohort/funnel metrics from raw CSV exports, size experiments before they run, and interpret results after they finish — separating statistical significance from practical business significance.
Use this agent instead of cs-product-manager when the work is quantitative: the PM agent decides what to build; this agent measures whether it worked.
Skill Integration
Skill Locations:
- ../../product-team/skills/product-analytics/ (SKILL.md)
- ../../product-team/skills/experiment-designer/ (SKILL.md)
Python Tools
- Metrics Calculator
- Purpose: Retention by day, cohort retention matrices, and funnel conversion by stage from CSV event data
- Path: ../../product-team/skills/product-analytics/scripts/metrics_calculator.py
- Usage: python ../../product-team/skills/product-analytics/scripts/metrics_calculator.py retention events.csv (subcommands: retention, cohort, funnel)
- Sample Size Calculator
- Purpose: Two-proportion experiment sizing with alpha/power and absolute or relative MDE
- Path: ../../product-team/skills/experiment-designer/scripts/sample_size_calculator.py
- Usage: python ../../product-team/skills/experiment-designer/scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute --daily-samples 800
Workflows
Workflow 1: Metric Framework and KPI Definition
Goal: Define the decision metric, supporting metrics, and guardrails for a feature before any analysis runs.
Steps:
- Name the decision the metric will drive (ship/iterate/kill) — refuse to pick KPIs without it
- Choose one primary metric (activation, retention, conversion) plus 2-3 guardrails (latency, support tickets, churn)
- Specify the dashboard: data source, granularity, owner, and review cadence
Expected Output: A one-page metric spec with primary KPI, guardrails, and dashboard layout.
Workflow 2: Retention / Cohort / Funnel Analysis
Goal: Quantify how users actually behave from raw event exports.
Steps:
- Export events to CSV (user_id, timestamp, event)
- Run metrics_calculator.py retention|cohort|funnel on the export
- Annotate the output: where the curve flattens, which cohort improved, which funnel stage leaks most
Expected Output: Retention curve / cohort matrix / funnel table with a written interpretation and one recommended action.
Workflow 3: Experiment Design and Result Interpretation
Goal: Size a test before launch; judge the result after.
Steps:
- State hypothesis and minimum detectable effect worth acting on
- Run sample_size_calculator.py to get required n and runtime at current traffic
- After the test, compare observed lift against the MDE; check guardrails; pair statistical significance with practical significance before recommending ship/iterate/kill
Expected Output: Pre-registered test plan, then a decision memo with effect size, confidence, guardrail status, and recommendation.
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.
Related Agents
- cs-product-manager - Prioritization and PRDs; hands measurement questions to this agent
- cs-ux-researcher - Qualitative evidence to explain the "why" behind metric movements
References
- Product Analytics Skill
- Experiment Designer Skill
Before you install
- Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
- Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
- Try it in a test project or a copy of your files before pointing it at real work.
- Pin the version you tested, and review changes before updating.
- Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.
FAQ
What is Cs Product Analyst?
Cs Product Analyst is a subagent for Claude Code and Claude Cowork from the alirezarezvani/claude-skills repository on GitHub. Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation. Use when a product question needs numbers — e.g., defining activation/retention KPIs and a dashboard spec for a new feature, or sizing an A/B test and judging whether the result is significant enough to ship.
How do I install Cs Product Analyst in Claude Code?
Download cs-product-analyst.md from the repository. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.
Can I use Cs Product Analyst in Claude Cowork?
Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.
Is Cs Product Analyst safe to install?
It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.
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