Files
claude-skills-reference/docs/agents/cs-product-analyst.md
Reza Rezvani 2f57ef8948 feat(agenthub): add AgentHub plugin with cross-domain examples, SEO optimization, and docs site fixes
- AgentHub: 13 files updated with non-engineering examples (content drafts,
  research, strategy) — engineering stays primary, cross-domain secondary
- AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent,
  dry_run.py validation (57 checks)
- Marketplace: agenthub entry added with cross-domain keywords, engineering
  POWERFUL updated (25→30), product (12→13), counts synced across all configs
- SEO: generate-docs.py now produces keyword-rich <title> tags and meta
  descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name
  propagates to all 276 HTML pages
- SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.),
  slug-as-title cleanup, domain label stripping from titles
- Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts
  references/, scripts/, assets/ links to GitHub source URLs; skills/index.md
  phantom slugs fixed (6 marketing, 7 RA/QM)
- Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands,
  21 plugins — consistent across CLAUDE.md, README.md, docs/index.md,
  marketplace.json, getting-started.md, mkdocs.yml
- Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-17 12:10:46 +01:00

1.6 KiB

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

Product Analyst Agent

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

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

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.