---
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".'
version: "1.0.0"
author: "seaworld008"
source: "in-house"
source_url: ""
tags: '["analysis", "design", "product"]'
created_at: "2026-03-04"
updated_at: "2026-03-20"
quality: 5
complexity: "intermediate"
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:

```bash
# Check if Codex CLI is installed
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:
```bash
# Detect project type
ls package.json 2>/dev/null    # Node.js/React
ls pyproject.toml 2>/dev/null  # Python
ls Cargo.toml 2>/dev/null      # Rust
ls go.mod 2>/dev/null          # Go
```

## 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:

```bash
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:

```markdown
## 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

- [ ] Parse `$ARGUMENTS` for scope
- [ ] Auto-detect Codex CLI availability (`which codex`)
- [ ] Auto-detect project type (package.json / pyproject.toml / etc.)
- [ ] Launch Claude Code Explore agents (3-5 parallel, background)
- [ ] Launch Codex CLI commands (2-3 parallel, background) if detected
- [ ] Invoke `/competitors-analysis` if `compare` scope
- [ ] Collect all agent results
- [ ] Cross-validate findings
- [ ] Quantify metrics
- [ ] Generate structured report with P0/P1/P2 priorities

## References

- [references/analysis_dimensions.md](references/analysis_dimensions.md) — Detailed audit dimension definitions and prompts
- [references/synthesis_methodology.md](references/synthesis_methodology.md) — How to weight and merge multi-agent findings
- [references/codex_patterns.md](references/codex_patterns.md) — Codex CLI invocation patterns and flag reference
