| name | product-analysis |
| description | Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计". |
| argument-hint | [scope: full|ux|api|arch|compare] |
Product Analysis
Multi-path parallel product analysis that combines Claude Code agent teams and Codex CLI for cross-model test-time compute scaling.
Core principle: Same analysis task, multiple AI perspectives, deep synthesis.
How It Works
/product-analysis full
│
├─ Step 0: Auto-detect available tools (codex? competitors?)
│
┌────┼──────────────┐
│ │ │
Claude Code Codex CLI (auto-detected)
Task Agents (background Bash)
(Explore ×3-5) (×2-3 parallel)
│ │
└────────┬──────────┘
│
Synthesis (main context)
│
Structured Report
Step 0: Auto-Detect Available Tools
Before launching any agents, detect what tools are available:
which codex 2>/dev/null && codex --version
Decision logic:
- If
codex is found: Inform the user — "Codex CLI detected (version X). Will run cross-model analysis for richer perspectives."
- If
codex is not found: Silently proceed with Claude Code agents only. Do NOT ask the user to install anything.
Also detect the project type to tailor agent prompts:
ls package.json 2>/dev/null
ls pyproject.toml 2>/dev/null
ls Cargo.toml 2>/dev/null
ls go.mod 2>/dev/null
Scope Modes
Parse $ARGUMENTS to determine analysis scope:
| Scope | What it covers | Typical agents |
|---|
full | UX + API + Architecture + Docs (default) | 5 Claude + Codex (if available) |
ux | Frontend navigation, information density, user journey, empty state, onboarding | 3 Claude + Codex (if available) |
api | Backend API coverage, endpoint health, error handling, consistency | 2 Claude + Codex (if available) |
arch | Module structure, dependency graph, code duplication, separation of concerns | 2 Claude + Codex (if available) |
compare X Y | Self-audit + competitive benchmarking (invokes /competitors-analysis) | 3 Claude + competitors-analysis |
Phase 1: Parallel Exploration
Launch all exploration agents simultaneously using Task tool (background mode).
Claude Code Agents (always)
For each dimension, spawn a Task agent with subagent_type: Explore and run_in_background: true:
Agent A — Frontend Navigation & Information Density
Explore the frontend navigation structure and entry points:
1. App.tsx: How many top-level components are mounted simultaneously?
2. Left sidebar: How many buttons/entries? What does each link to?
3. Right sidebar: How many tabs? How many sections per tab?
4. Floating panels: How many drawers/modals? Which overlap in functionality?
5. Count total first-screen interactive elements for a new user.
6. Identify duplicate entry points (same feature accessible from 2+ places).
Give specific file paths, line numbers, and element counts.
Agent B — User Journey & Empty State
Explore the new user experience:
1. Empty state page: What does a user with no sessions see? Count clickable elements.
2. Onboarding flow: How many steps? What information is presented?
3. Prompt input area: How many buttons/controls surround the input box? Which are high-frequency vs low-frequency?
4. Mobile adaptation: How many nav items? How does it differ from desktop?
5. Estimate: Can a new user complete their first conversation in 3 minutes?
Give specific file paths, line numbers, and UX assessment.
Agent C — Backend API & Health
Explore the backend API surface:
1. List ALL API endpoints (method + path + purpose).
2. Identify endpoints that are unused or have no frontend consumer.
3. Check error handling consistency (do all endpoints return structured errors?).
4. Check authentication/authorization patterns (which endpoints require auth?).
5. Identify any endpoints that duplicate functionality.
Give specific file paths and line numbers.
Agent D — Architecture & Module Structure (full/arch scope only)
Explore the module structure and dependencies:
1. Map the module dependency graph (which modules import which).
2. Identify circular dependencies or tight coupling.
3. Find code duplication across modules (same pattern in 3+ places).
4. Check separation of concerns (does each module have a single responsibility?).
5. Identify dead code or unused exports.
Give specific file paths and line numbers.
Agent E — Documentation & Config Consistency (full scope only)
Explore documentation and configuration:
1. Compare README claims vs actual implemented features.
2. Check config file consistency (base.yaml vs .env.example vs code defaults).
3. Find outdated documentation (references to removed features/files).
4. Check test coverage gaps (which modules have no tests?).
Give specific file paths and line numbers.
Codex CLI Agents (auto-detected)
If Codex CLI was detected in Step 0, launch parallel Codex analyses via background Bash.
Each Codex invocation gets the same dimensional prompt but from a different model's perspective:
codex -m o4-mini \
-c model_reasoning_effort="high" \
--full-auto \
"Analyze the frontend navigation structure of this project. Count all interactive elements visible to a new user on first screen. Identify duplicate entry points where the same feature is accessible from 2+ places. Give specific file paths and counts."
Run 2-3 Codex commands in parallel (background Bash), one per major dimension.
Important: Codex runs in the project's working directory. It has full filesystem access. The --full-auto flag (or --dangerously-bypass-approvals-and-sandbox for older versions) enables autonomous execution.
Phase 2: Competitive Benchmarking (compare scope only)
When scope is compare, invoke the competitors-analysis skill for each competitor:
Use the Skill tool to invoke: /competitors-analysis {competitor-name} {competitor-url}
This delegates to the orthogonal competitors-analysis skill which handles:
- Repository cloning and validation
- Evidence-based code analysis (file:line citations)
- Competitor profile generation
Phase 3: Synthesis
After all agents complete, synthesize findings in the main conversation context.
Cross-Validation
Compare findings across agents (Claude vs Claude, Claude vs Codex):
- Agreement = high confidence finding
- Disagreement = investigate deeper (one agent may have missed context)
- Codex-only finding = different model perspective, validate manually
Quantification
Extract hard numbers from agent reports:
| Metric | What to measure |
|---|
| First-screen interactive elements | Total count of buttons/links/inputs visible to new user |
| Feature entry point duplication | Number of features with 2+ entry points |
| API endpoints without frontend consumer | Count of unused backend routes |
| Onboarding steps to first value | Steps from launch to first successful action |
| Module coupling score | Number of circular or bi-directional dependencies |
Structured Output
Produce a layered optimization report:
## Product Analysis Report
### Executive Summary
[1-2 sentences: key finding]
### Quantified Findings
| Metric | Value | Assessment |
|--------|-------|------------|
| ... | ... | ... |
### P0: Critical (block launch)
[Issues that prevent basic usability]
### P1: High Priority (launch week)
[Issues that significantly degrade experience]
### P2: Medium Priority (next sprint)
[Issues worth addressing but not blocking]
### Cross-Model Insights
[Findings that only one model identified — worth investigating]
### Competitive Position (if compare scope)
[How we compare on key dimensions]
Workflow Checklist
References
