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".
Instrucciones de origen · Vista previa de solo lectura
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".
source
daymade
argument-hint
[{"scope":"full|ux|api|arch|compare"}]
trigger
需要全面的产品分析
input
产品信息
output
分析报告
next
无
dependencies
无
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.
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.
