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