product-analysis（产品分析）

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:

# 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:

# 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:

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:

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

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