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- 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
88 lines
5.1 KiB
Markdown
88 lines
5.1 KiB
Markdown
---
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title: "Product Analyst Agent — AI Coding Agent & Codex Skill"
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description: "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."
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---
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# Product Analyst Agent
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-robot: Agent</span>
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<span class="meta-badge">:material-lightbulb-outline: Product</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/agents/product/cs-product-analyst.md">Source</a></span>
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</div>
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## Purpose
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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.
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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*.
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## Skill Integration
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**Skill Locations:**
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- [`skills/product-analytics`](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics) ([SKILL.md](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics/SKILL.md))
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- [`skills/experiment-designer`](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/experiment-designer) ([SKILL.md](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/experiment-designer/SKILL.md))
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### Python Tools
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1. **Metrics Calculator**
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- **Purpose:** Retention by day, cohort retention matrices, and funnel conversion by stage from CSV event data
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- **Path:** [`scripts/metrics_calculator.py`](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics/scripts/metrics_calculator.py)
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- **Usage:** `python ../../product-team/skills/product-analytics/scripts/metrics_calculator.py retention events.csv` (subcommands: `retention`, `cohort`, `funnel`)
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2. **Sample Size Calculator**
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- **Purpose:** Two-proportion experiment sizing with alpha/power and absolute or relative MDE
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- **Path:** [`scripts/sample_size_calculator.py`](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/experiment-designer/scripts/sample_size_calculator.py)
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- **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`
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## Workflows
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### Workflow 1: Metric Framework and KPI Definition
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**Goal:** Define the decision metric, supporting metrics, and guardrails for a feature before any analysis runs.
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**Steps:**
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1. **Name the decision** the metric will drive (ship/iterate/kill) — refuse to pick KPIs without it
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2. **Choose one primary metric** (activation, retention, conversion) plus 2-3 guardrails (latency, support tickets, churn)
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3. **Specify the dashboard**: data source, granularity, owner, and review cadence
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**Expected Output:** A one-page metric spec with primary KPI, guardrails, and dashboard layout.
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### Workflow 2: Retention / Cohort / Funnel Analysis
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**Goal:** Quantify how users actually behave from raw event exports.
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**Steps:**
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1. Export events to CSV (user_id, timestamp, event)
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2. Run `metrics_calculator.py retention|cohort|funnel` on the export
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3. Annotate the output: where the curve flattens, which cohort improved, which funnel stage leaks most
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**Expected Output:** Retention curve / cohort matrix / funnel table with a written interpretation and one recommended action.
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### Workflow 3: Experiment Design and Result Interpretation
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**Goal:** Size a test before launch; judge the result after.
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**Steps:**
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1. State hypothesis and minimum detectable effect worth acting on
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2. Run `sample_size_calculator.py` to get required n and runtime at current traffic
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3. After the test, compare observed lift against the MDE; check guardrails; pair statistical significance with practical significance before recommending ship/iterate/kill
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**Expected Output:** Pre-registered test plan, then a decision memo with effect size, confidence, guardrail status, and recommendation.
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## Usage Notes
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- Define decision metrics before analysis to avoid post-hoc bias.
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- Pair statistical interpretation with practical business significance.
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- Use guardrail metrics to prevent local optimization mistakes.
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## Related Agents
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- [cs-product-manager](cs-product-manager.md) - Prioritization and PRDs; hands measurement questions to this agent
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- [cs-ux-researcher](cs-ux-researcher.md) - Qualitative evidence to explain the "why" behind metric movements
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## References
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- [Product Analytics Skill](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics/SKILL.md)
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- [Experiment Designer Skill](https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/experiment-designer/SKILL.md)
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