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claude-skills/docs/agents/cs-product-analyst.md
Claude 82c5aea9f0
Merge origin/dev: reconcile docs redesign with upstream skill changes 
- Resolve conflicts: keep redesigned skills index, take dev's cs-aeo link
  fix, union of DOMAIN_SEO_CONTEXT entries in generate-docs.py
- Regenerate catalog on the merged tree (dev's agent/command description
  updates, removed ai-seo/release-manager/command-guide, restructured
  universal-scraping-architect)
- Update counters to post-merge truth from scripts/derive_counters.py:
  345 skills, 78 plugins (14 bundles + 64 standalone), 570+ Python tools
- Add redirects for upstream-removed pages (ai-seo -> aeo,
  release-manager -> changelog-generator, command-guide -> engineering index)
- Add compliance-os bundle to bundle tables; rebuild 78-plugin table from
  marketplace.json
- Teach the generator to rewrite repo-root-relative source links to GitHub
  URLs — mkdocs build --strict now passes with zero warnings

https://claude.ai/code/session_015bYZ97nV4oRb3LbxCRFVcP
2026-06-11 15:52:42 +00:00

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title description
Product Analyst Agent — AI Coding Agent & Codex Skill Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation. Use when a product question needs. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Product Analyst Agent

:material-robot: Agent :material-lightbulb-outline: Product :material-github: Source

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:

Python Tools

  1. Metrics Calculator

    • Purpose: Retention by day, cohort retention matrices, and funnel conversion by stage from CSV event data
    • Path: scripts/metrics_calculator.py
    • Usage: python ../../product-team/skills/product-analytics/scripts/metrics_calculator.py retention events.csv (subcommands: retention, cohort, funnel)

  2. Sample Size Calculator

    • Purpose: Two-proportion experiment sizing with alpha/power and absolute or relative MDE
    • Path: 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:

  1. Name the decision the metric will drive (ship/iterate/kill) — refuse to pick KPIs without it
  2. Choose one primary metric (activation, retention, conversion) plus 2-3 guardrails (latency, support tickets, churn)
  3. 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:

  1. Export events to CSV (user_id, timestamp, event)
  2. Run metrics_calculator.py retention|cohort|funnel on the export
  3. 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:

  1. State hypothesis and minimum detectable effect worth acting on
  2. Run sample_size_calculator.py to get required n and runtime at current traffic
  3. 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