name

main-router

description

Intelligent skill router that analyzes user requests and automatically dispatches to the most appropriate skill(s) or zen-mcp tools. Routes to zen-chat for Q&A, zen-thinkdeep for deep problem investigation, codex-code-reviewer for code quality, simple-gemini for standard docs/tests, deep-gemini for deep analysis, or plan-down for planning. Use this skill proactively to interpret all user requests and determine the optimal execution path.

Main Router - Intelligent Skill Routing Scheduler

Overview

This skill serves as the central intelligence hub that analyzes user requests and automatically routes them to the most appropriate skill(s) for execution. It acts as a smart dispatcher, understanding user intent and orchestrating the right tools for the job.

Core Capabilities:

Standards-based routing (follows CLAUDE.md)
Intent analysis and classification
Skill matching and selection
Multi-skill orchestration (sequential or parallel)
Conflict resolution and disambiguation
Automatic routing without user intervention
Full automation mode support (router makes decisions autonomously)

Division of Responsibilities:

Main Router: Analyzes request → Reads standards → Determines skill(s) → Invokes skill(s) → Coordinates execution
Specialized Skills: Execute their specific tasks when invoked by router

Standards Compliance:

MUST read global and project CLAUDE.md before routing
Apply standards hierarchy: Global CLAUDE.md > Project CLAUDE.md
All routing decisions must align with documented rules and workflows

Active Task Monitoring (CRITICAL - Router Must Not Be Lazy):

Main Router MUST actively monitor the entire task lifecycle and proactively invoke appropriate skills at each stage. Do NOT skip skill invocations to save time - proper skill usage ensures quality and compliance.

Mandatory Workflow Rules:

Planning Phase:
- When user requests "make a plan" / "generate plan.md" / "plan tasks"
- MUST use plan-down skill (not Main Claude direct planning)
- Rationale: plan-down provides multi-model validation and structured decomposition
Code Generation → Quality Check Cycle:
- After Main Claude completes ANY code generation/modification
- MUST invoke codex-code-reviewer to validate quality
- Rationale: Ensures 5-dimension quality check (quality, security, performance, architecture, docs)
Test Code Generation Workflow:
- When Main Claude needs test code
- Step 1: MUST invoke simple-gemini to generate test files
- Step 2: MUST invoke codex-code-reviewer to validate generated tests
- Step 3: Return validated tests to Main Claude for execution
- Rationale: Ensures test quality before execution
Documentation Generation:
- Standard docs (README, PROJECTWIKI, CHANGELOG) → simple-gemini
- Deep analysis docs (architecture, performance) → deep-gemini
- Rationale: Specialized skills produce higher quality, standards-compliant docs
Continuous Monitoring:
- Router monitors task progress throughout execution
- Proactively suggests skill invocations when opportunities arise
- Example: "Just finished code, should I use codex to check quality?"

Anti-Pattern - Router Being Lazy (FORBIDDEN):

 BAD: Main Claude generates code → Main Claude self-reviews → Done
 GOOD: Main Claude generates code → Router invokes codex-code-reviewer → Done

 BAD: Main Claude writes plan.md directly
 GOOD: Router invokes plan-down skill → plan.md generated with validation

 BAD: Main Claude generates tests → Run immediately
 GOOD: Router invokes simple-gemini → codex validates → Main Claude runs

When to Use This Skill

Use this skill PROACTIVELY for ALL user requests to determine the best execution path.

Typical User Requests:

"Explain what is..." (→ zen-chat)
"Deep analysis of problem..." (→ zen-thinkdeep)
"Help me check code" (→ codex-code-reviewer)
"Generate README documentation" (→ simple-gemini)
"Deep performance analysis of this code" (→ deep-gemini)
"Make development plan" (→ plan-down)
"Write test files" (→ simple-gemini)
"Generate architecture analysis document" (→ deep-gemini)
Any task-related request or Q&A

Router's Decision Process:

User Request → Read Standards (CLAUDE.md) → Intent Analysis → Skill Matching → Auto/Manual Decision → Execution

Operation Modes:

Interactive Mode (Default):
- Router asks user for clarification when ambiguous
- User makes final decisions on skill selection
- Router provides recommendations with rationale
Full Automation Mode (automation_mode - READ FROM SSOT):

automation_mode definition and constraints: See CLAUDE.md「📚 共享概念速查」

This skill's role (Router Layer - Sole Source):
- Judge and set automation_mode at task start (detect keywords: "full automation", "automatic process", etc.)
- Set status: automation_mode = true/false
- Transmit to downstream: [AUTOMATION_MODE: true/false]
- Monitor throughout lifecycle, enforce mandatory skill invocations (plan-down/codex/simple-gemini)

Available Skills Registry

0. zen-chat (Direct Tool)

Purpose: General Q&A and collaborative thinking partner

Triggers:

"Explain..."
"What is..."
"How to understand..."
"Help me analyze..." (non-technical deep analysis)
General questions, brainstorming, explanations

Use Cases:

Answer conceptual questions
Explain programming concepts
Brainstorming ideas
Quick clarifications
Thoughtful explanations

Key Features:

Fast, direct responses
No file operations needed
Conversation-based
Supports multi-turn discussions

Tool: mcp__zen__chat (direct invocation, not a packaged skill)

0.5. zen-thinkdeep (Direct Tool)

Purpose: Multi-stage investigation and reasoning for complex problem analysis

Triggers:

"Deep analysis of problem..."
"Investigate root cause of this bug..."
"Systematic analysis..." (technical deep dive)
"Complex problem analysis..."
Architecture decisions, complex bugs, performance challenges

Use Cases:

Complex bug investigation
Architecture decision analysis
Performance bottleneck deep dive
Security analysis
Systematic hypothesis testing

Key Features:

Multi-stage investigation workflow
Hypothesis-driven analysis
Evidence-based findings
Expert validation
Comprehensive problem-solving

Tool: mcp__zen__thinkdeep (direct invocation, not a packaged skill)

1. codex-code-reviewer

Purpose: Code quality review with iterative fix-and-recheck cycles

Triggers:

"Use codex to check code"
"Check if the just-generated code has problems"
"Check code after every generation"
"Code review"
"Code quality check"

Use Cases:

Post-development code quality validation
Pre-commit code review
Bug fix verification
Refactoring quality assurance

Key Features:

5-dimension quality check (quality, security, performance, architecture, documentation)
Iterative fix cycles (max 5 iterations)
User approval required before fixes
Based on CLAUDE.md standards

Tool: mcp__zen__codereview

2. simple-gemini

Purpose: Standard documentation and test code generation

Triggers:

"Use gemini to write test files"
"Use gemini to write documentation"
"Generate README"
"Generate PROJECTWIKI"
"Generate CHANGELOG"
"Write test code"

Use Cases:

Generate standard project documentation (PROJECTWIKI, README, CHANGELOG, ADR)
Write test code files
Create project templates
Standard documentation maintenance

Key Features:

Two modes: Interactive (default) and Automated
Document types: PROJECTWIKI, README, CHANGELOG, ADR, plan.md
Test code generation with codex validation
Follows CLAUDE.md standards

Tool: mcp__zen__clink (launches gemini CLI in WSL)

3. deep-gemini

Purpose: Deep technical analysis documents with complexity evaluation

Triggers:

"Use gemini for deep code logic analysis"
"Generate architecture analysis document"
"Analyze performance bottlenecks and generate report"
"Deep understanding of this code and generate documentation"
"Generate model architecture analysis"

Use Cases:

Code logic deep dive
Model architecture analysis
Performance bottleneck analysis
Technical debt assessment
Security analysis report

Key Features:

Two-stage workflow: clink (Gemini CLI analysis) → docgen (dual-phase document generation)
Big O complexity analysis included (docgen core capability)
Automatic Mermaid diagram generation
Evidence-based findings
Professional technical writing

Tools: mcp__zen__clink + mcp__zen__docgen

docgen workflow:

Step 1: Exploration (explore project structure, formulate documentation plan)
Step 2+: Per-File Documentation (generate structured docs with complexity analysis)

4. plan-down ⭐ MANDATORY for Planning

Purpose: Intelligent planning with task decomposition and multi-model validation

CRITICAL: This skill is MANDATORY for all plan.md generation tasks

Main Claude must NOT generate plan.md directly
Router MUST invoke plan-down for all planning requests
Rationale: Ensures multi-model validation and structured decomposition

Triggers:

"Help me make a plan"
"Generate plan.md"
"Use planner for task planning"
"Help me break down tasks"
"Make implementation plan"
"Plan the project"

Use Cases:

Feature development planning
Project implementation roadmaps
Refactoring strategies
Migration plans
Complex task breakdown

Key Features:

Two-stage workflow: planner (decomposition) → consensus (validation)
Multi-model evaluation (codex, gemini, gpt-5)
Standards-based planning (CLAUDE.md)
Mermaid dependency graphs
Risk assessment tables

Tools: mcp__zen__chat (Phase 0 method clarity judgment) + mcp__zen__planner + mcp__zen__consensus (conditional - only for Automatic + Unclear path) + mcp__zen__clink (when using consensus with codex/gemini)

Model Support (G10 Compliance - CRITICAL):

codex/gemini: MUST use mcp__zen__clink to establish CLI session first (otherwise 401 error)
Other models: Direct API access
Detailed standards: See references/standards/cli_env_g10.md

Enforcement:

IF user requests planning OR plan.md generation:
    MUST route to plan-down
    NEVER allow Main Claude to create plan.md directly

Reason: plan-down provides superior planning quality through:
- Multi-stage interactive planning
- Multi-model consensus validation
- Standards compliance verification
- Risk assessment and dependency analysis

5. gemini-frontend ⭐ MANDATORY for Frontend/Mobile Development

Purpose: Frontend and mobile development specialist using Gemini CLI with multimodal capabilities

适用场景：

React/Vue/Angular 组件开发
React Native/Flutter 移动端开发
设计稿 → 前端代码实现（multimodal）
UI/UX 实现和优化
前端项目重构

Triggers:

"Help me build a React component"
"Generate Vue/Angular code"
"Convert this design to code" (with image)
"Implement this UI feature"
"Mobile app development" (React Native/Flutter)
Keywords: React, Vue, Angular, component, 组件, 页面, UI, 前端, mobile, Flutter

Core Advantages (Based on Gemini 3.0):

📷 Multimodal Capability: Directly understand design mockups and UI screenshots
📚 Ultra-long Context: 1M tokens, handles large monorepos
🎨 UI Understanding: PhD-level reasoning for complex UI logic
🚀 Code Generation: Excels at React/Vue/Flutter code generation

Use Cases:

Design-to-code conversion
Component library development
Mobile UI implementation
Frontend architecture setup
State management implementation

Key Features:

5-phase workflow (Init → Analysis → Generation → Quality Check → Documentation)
Dual quality validation (codereview + clink CLI) - complies with G8
Mermaid diagram updates - complies with G4
Environment-adaptive CLI calls - complies with G10
Inherits automation_mode and coverage_target from router

Tools: mcp__zen__clink (gemini CLI) + mcp__zen__codereview + simple-gemini

Frontend Detection Scoring:

Tier 1 Keywords (+30-35 points): React, Vue, Angular, component, 组件, 页面, UI, 前端
Tier 2 Keywords (+15-20 points): Flutter, React Native, mobile, 移动端, iOS, Android
Tier 3 Context (+10 points): package.json exists with frontend dependencies
Image Attachment (+25 points): Design mockups, UI screenshots
Backend Signal Penalty (-15 to -25 points): API, backend, database, FastAPI, Django

Routing Thresholds:

Score ≥ 80: Auto-route to gemini-frontend
Score 50-79: Ask user confirmation
Score < 50 AND backend_signals ≥ 2: Fullstack project, suggest task decomposition

Enforcement:

IF frontend_score ≥ 80:
    Auto-route to gemini-frontend (high confidence)

IF 50 ≤ frontend_score < 80:
    Ask user: "检测到前端开发需求，是否使用 gemini-frontend?"

IF frontend_score < 50 AND backend_signals ≥ 2:
    Notify user: "检测到全栈项目，建议任务分解：
    - 前端部分 → gemini-frontend
    - 后端部分 → codex-code-reviewer 或其他技能"

Detailed Examples:

Frontend Request Scoring: See Example 12 - Frontend Development Request for detailed demonstration of Tier 1-3 keyword detection and auto-routing (Score: 90 → gemini-frontend)
Fullstack Project Handling: See Example 13 - Fullstack Project Detection for task decomposition strategy when both frontend and backend signals detected (Score: 45 with backend penalty → recommend split)

Routing Decision Logic

Phase 0: Standards Reading & MCP Discovery (CRITICAL - First Step)

Main Router's Action:

Before ANY routing decision, MUST complete the following two sub-phases:

Phase 0.1: Read Standards

Read the following files to understand project-specific rules and workflows:

a) Read Global Standards:

Global CLAUDE.md: /home/vc/.claude/CLAUDE.md
- Extract: Global rules (G1-G11), phase requirements (P1-P4), routing mechanism, core principles
- Key focus: Which phase user is in, execution permissions, documentation requirements
- Extract: Model development workflow, ethics principles, reproducibility requirements

b) Read Project-Specific Standards (if exist):

Project CLAUDE.md: ./CLAUDE.md (current directory)
- Extract: Project-specific rules, custom workflows, overrides

Standards Priority Hierarchy (when conflicts):

Global CLAUDE.md (highest priority)
Project CLAUDE.md (overrides global)
PROJECTWIKI.md (lowest priority)

Phase 0.2: 动态 MCP 发现与智能适配（Dynamic MCP Discovery & Adaptation）

核心原则：不假设任何 MCP 工具"一定存在"，运行时动态检测并智能适配。

MCP 环境扫描策略

检测时机：

会话启动时：首次路由前执行快速扫描（可选，推荐）
运行时检测：技能调用 MCP 工具时懒加载检测（默认）
失败触发：MCP 调用失败时重新扫描并更新缓存

检测方法：

# 伪代码示例 - 动态 MCP 能力检测

mcp_capabilities = {}  # 会话级缓存

def detect_mcp_availability():
    """运行时检测 MCP 工具可用性"""

    # 1. 检测 zen-mcp
    try:
        version_info = call_tool("mcp__zen__version")
        mcp_capabilities["zen-mcp"] = {
            "available": True,
            "version": version_info.get("version"),
            "tools": extract_available_tools(version_info)
        }
    except Exception:
        mcp_capabilities["zen-mcp"] = {"available": False}

    # 2. 检测 serena-mcp（代码智能）
    try:
        config = call_tool("mcp__serena__get_current_config")
        mcp_capabilities["serena-mcp"] = {
            "available": True,
            "tools": list_serena_tools()
        }
    except Exception:
        mcp_capabilities["serena-mcp"] = {"available": False}

    # 3. 检测 unifuncs-mcp（工具函数）
    try:
        search_test = call_tool("mcp__unifuncs__web-search", {"query": "test", "count": 1})
        mcp_capabilities["unifuncs-mcp"] = {"available": True}
    except Exception:
        mcp_capabilities["unifuncs-mcp"] = {"available": False}

    # 4. 检测其他 MCP（用户自定义）
    # 可通过 ListMcpResourcesTool 发现额外 MCP 服务器

    return mcp_capabilities

技能 → MCP 依赖关系（动态映射）

技能与 MCP 工具的依赖分为三类：

必需工具（Required）：技能核心功能依赖，缺失则无法工作
增强工具（Enhancement）：提升技能能力，缺失可降级
可选工具（Optional）：锦上添花，缺失无影响

动态依赖映射表：

Skill	必需工具	增强工具	降级方案
zen-chat	mcp__zen__chat	mcp__zen__apilookup mcp__unifuncs__web-search	降级到主模型直接回答（无多轮协作）
zen-thinkdeep	mcp__zen__thinkdeep	mcp__serena__* (代码分析) mcp__zen__debug	降级到主模型单轮深度分析
codex-code-reviewer	mcp__zen__codereview 或 mcp__zen__clink (codex CLI)	mcp__serena__* (符号编辑) mcp__zen__precommit	使用主模型 + Read/Edit 工具进行审查
simple-gemini	mcp__zen__clink (gemini CLI)	mcp__serena__* (代码读取) mcp__unifuncs__web-reader	降级到主模型直接生成文档/测试
deep-gemini	mcp__zen__clink (gemini CLI) mcp__zen__docgen	mcp__serena__* (代码分析) mcp__zen__apilookup	降级到主模型深度分析
plan-down	mcp__zen__chat (方法判断) mcp__zen__planner (任务分解)	mcp__zen__consensus (自动化模式) mcp__serena__read_memory (项目上下文) mcp__zen__clink (codex/gemini CLI)	降级到主模型直接规划
gemini-frontend	mcp__zen__clink (gemini CLI)	mcp__serena__* (代码分析) mcp__unifuncs__web-reader (设计参考)	降级到主模型前端开发

G10 合规特殊要求：

使用 codex/gemini 模型时，必须先用 mcp__zen__clink 建立 CLI 会话
plan-down 的四路径工作流：Phase 0 使用 chat 判断方法清晰度，Automatic + Unclear 路径需要 consensus

智能适配与降级策略

适配原则：

用户显式指定 MCP 工具时：
- 优先尝试用户指定的工具
- 如果工具不可用，通知用户并提供替代方案
- 示例：用户说 "use serena to analyze code" → 检测 serena → 不可用则通知
Router 自动选择技能时：
- 根据 MCP 可用性调整技能推荐优先级
- 必需工具不可用 → 降级到备用方案
- 仅增强工具不可用 → 静默降级，不通知用户
降级决策树：

IF 技能必需工具全部可用:
    → 正常路由到该技能（最优方案）

ELSE IF 技能必需工具部分缺失:
    → 检查降级方案是否可行
    IF 降级方案可行:
        → 使用降级方案（通知用户，如果是显式请求）
    ELSE:
        → 通知用户工具缺失，请求确认或提供替代方案

ELSE IF 仅增强工具缺失:
    → 正常路由，静默降级（不通知用户）

降级方案示例：

原方案	缺失工具	降级方案	通知用户？
codex-code-reviewer	zen-mcp 完全不可用	主模型 + Read/Edit 工具审查	✅ 是（显著功能降级）
simple-gemini	clink 不可用	主模型直接生成文档	✅ 是（质量可能下降）
zen-chat	zen__apilookup 不可用	仅使用 zen__chat，无 API 查询	❌ 否（增强功能，非必需）
zen-thinkdeep	serena 不可用	使用 Read/Grep 工具代替代码分析	❌ 否（自动适配）

运行时适配示例

示例 1：用户显式请求使用 codex

用户："use codex to check the code"

Router 执行：
1. 检测 zen-mcp 可用性
   - IF zen-mcp 可用 → 路由到 codex-code-reviewer（使用 mcp__zen__codereview）
   - IF zen-mcp 不可用但 clink 可用 → 路由到 codex-code-reviewer（使用 mcp__zen__clink + codex CLI）
   - IF 两者都不可用 → 通知用户：
     "检测到 zen-mcp 和 clink 均不可用。可以使用主模型进行代码审查（功能受限），是否继续？"

示例 2：Router 自动路由到 simple-gemini

Router 判断：需要生成 README 文档 → 路由到 simple-gemini

适配流程：
1. 检测 mcp__zen__clink 可用性
   - IF 可用 → 正常调用 simple-gemini（使用 gemini CLI）
   - IF 不可用 → 降级到主模型直接生成（通知用户："gemini CLI 不可用，使用主模型生成文档"）

2. 检测增强工具（serena, unifuncs）
   - IF serena 可用 → 增强代码读取能力
   - IF serena 不可用 → 使用 Read 工具（静默降级，不通知）

示例 3：全自动化模式下的 plan-down

Router 判断：P2 阶段，需要生成 plan.md → 路由到 plan-down

适配流程：
1. 检测必需工具（chat, planner）
   - IF 全部可用 → 继续
   - IF 任一缺失 → 降级到主模型直接规划（通知："plan-down 依赖工具缺失，使用主模型规划"）

2. 检测增强工具（consensus, clink）
   - IF automation_mode=true 且方法模糊 → 需要 consensus
     - consensus 可用 → 正常多模型验证
     - consensus 不可用 → 降级到单模型规划（通知："多模型验证不可用，使用单模型规划"）
   - IF consensus 需要 codex/gemini → 检测 clink
     - clink 可用 → 符合 G10，建立 CLI 会话
     - clink 不可用 → 跳过 consensus（静默降级）

MCP 可用性缓存与刷新

缓存策略：

会话级缓存：检测结果在同一会话中共享
失败触发刷新：MCP 调用失败时自动重新检测
手动刷新：用户可请求 "refresh MCP status" 强制重新扫描

缓存数据结构：

# 示例缓存结构
mcp_status_cache = {
    "zen-mcp": {
        "available": True,
        "last_check": "2025-11-19T11:30:00Z",
        "tools": ["chat", "thinkdeep", "codereview", "clink", "planner", ...]
    },
    "serena-mcp": {
        "available": True,
        "last_check": "2025-11-19T11:30:00Z",
        "tools": ["list_dir", "find_file", "search_for_pattern", ...]
    },
    "unifuncs-mcp": {
        "available": False,  # 用户未安装
        "last_check": "2025-11-19T11:30:00Z",
        "error": "Connection refused"
    }
}

Standards-Based Routing Rules（基于标准的路由规则）

根据 CLAUDE.md 阶段和 MCP 可用性动态调整路由：

P1 (分析问题) → 优先 zen-thinkdeep（如可用），否则主模型分析
P2 (制定方案) → 优先 plan-down（如可用），否则主模型规划
P3 (执行方案) → 代码完成后强制 codex-code-reviewer（如可用），否则主模型审查
automation_mode=true → 优先使用 MCP 增强的自动化工作流
文档生成需求 → 优先 simple-gemini/deep-gemini（如 clink 可用），否则主模型生成
G3 违规（禁止执行） → 不路由到任何执行相关技能

透明通知原则：

关键功能降级（必需工具缺失） → 必须通知用户
增强功能降级（可选工具缺失） → 静默适配，不通知
用户显式请求的工具缺失 → 必须通知并提供替代方案

Phase 0.3: Set Coverage Target (G9 Compliance)

coverage_target definition and constraints: See CLAUDE.md「📚 共享概念速查」

This skill's role (Router Layer - Sole Setting Source):

Ask user in P1/P2 phase (or use default 85%)
Inquiry script: "Suggest 85%, minimum 70%. Default 85% if unsure."
Transmit to downstream: [COVERAGE_TARGET: X%]
Record to plan.md (acceptance criteria)

Fixed Routing Rules (MANDATORY - Auto-Trigger)

These rules MUST be applied automatically at specific workflow points:

Rule 1: plan.md Generation → plan-down (MANDATORY)

Trigger: User requests "make a plan" / "generate plan.md" / "plan tasks"
Action: MUST use plan-down skill, FORBIDDEN for main model to write plan.md directly
Reason: plan-down provides multi-model validation, structured decomposition, standards compliance

Rule 2: Code Completed → codex-code-reviewer (MANDATORY)

Trigger: Main model completes any code generation or modification
Action: MUST use codex-code-reviewer for 5-dimension quality check (quality, security, performance, architecture, documentation)
Reason: Ensure code quality meets standards

Rule 3: Test Code Needed → Workflow (MANDATORY)

Trigger: Need to generate test code
Action:
- Step 1: Use simple-gemini to generate test files (pass [COVERAGE_TARGET: X%])
- Step 2: Use codex-code-reviewer to validate test code quality (pass [COVERAGE_TARGET: X%])
- Step 3: Main model executes tests
Reason: Ensure test code itself is correct and high-quality

Rule 4: Documentation Needed → Skill-Based (MANDATORY)

Trigger: Need to generate/update documentation
Action:
- Standard docs (README, PROJECTWIKI, CHANGELOG): Use simple-gemini
- Deep analysis docs (architecture, performance): Use deep-gemini
Reason: Specialized skills produce higher quality, standards-compliant documents

Rule 5: P3 Code Changes → Document Linkage (MANDATORY)

Trigger: Code changes in P3 (Execute Solution) phase
Action:
- Update PROJECTWIKI.md (affected modules/interfaces/flows)
- Update CHANGELOG.md (new entry with commit SHA)
- Establish bidirectional links (PROJECTWIKI ↔ CHANGELOG)
Reason: G1 compliance (documentation first-class citizen)

Rule 6: P4 Error Fixed → Regression Gate (MANDATORY)

Trigger: P4 (Error Handling) phase completes bug fix
Action (3-step validation, cannot skip):
- Step 1: Use mcp__zen__codereview for workflow validation
- Step 2: Use mcp__zen__clink (codex CLI) for deep code analysis
- Step 3: Verify document linkage:
  - PROJECTWIKI.md updated (design decisions & technical debt section includes defect postmortem)
  - CHANGELOG.md updated (Fixed section with repair summary)
  - Bidirectional links established
Reason: G8 compliance (mandatory double-pass validation), prevent hasty fixes

Anti-Lazy Principle:

Main-router MUST actively monitor task lifecycle
At each critical node, think: "Should I invoke a skill here?"
ABSOLUTELY FORBIDDEN: Skip skill invocation to "save effort", letting main model handle tasks that specialized skills should complete

Phase 1: Intent Classification

Main Router's Action:

Analyze the user request to identify:

Primary Intent:
- General Q&A / explanations → zen-chat
- Complex problem investigation → zen-thinkdeep
- Code review / quality check → codex-code-reviewer
- Standard documentation → simple-gemini
- Deep technical analysis → deep-gemini
- Planning / task breakdown → plan-down
- Other (direct execution by Main Claude)
Request Characteristics:
- Target: What is the subject? (code files, documentation, architecture, plan)
- Action: What needs to be done? (check, generate, analyze, plan)
- Depth: Surface-level or deep analysis?
- Output: What is expected? (report, document, plan, fixed code)
Context Signals:
- Keywords in user message
- Recently modified files (git status)
- Existing project state (has plan.md? has PROJECTWIKI.md?)
- User's workflow stage (development, review, planning)

Phase 2: Skill Matching

Decision Tree:

IF user asks general question ("explain", "what is", "how to understand"):
    → zen-chat

ELSE IF user requests deep problem analysis ("deep problem analysis", "investigate bug", "systematic analysis"):
    → zen-thinkdeep

ELSE IF user mentions "codex" OR "code check" OR "code review":
    → codex-code-reviewer

ELSE IF user mentions "gemini" AND ("documentation" OR "test"):
    IF mentions "deep" OR "analysis" OR "architecture" OR "performance":
        → deep-gemini
    ELSE:
        → simple-gemini

ELSE IF user mentions "planning" OR "plan" OR "roadmap":
    → plan-down

ELSE IF intent is "code review":
    → codex-code-reviewer

ELSE IF intent is "document generation":
    IF document type in [README, PROJECTWIKI, CHANGELOG, test]:
        → simple-gemini
    ELSE IF analysis type in [architecture, performance, code logic]:
        → deep-gemini

ELSE IF intent is "planning":
    → plan-down

ELSE IF intent is "Q&A" (no code/file operations):
    → zen-chat

ELSE:
    → Main Claude (direct execution, no skill routing)

Confidence Scoring:

For each tool/skill, calculate confidence score (0-100):

confidence_scores = {
    "zen-chat": calculate_qa_confidence(request),
    "zen-thinkdeep": calculate_deep_investigation_confidence(request),
    "codex-code-reviewer": calculate_code_review_confidence(request),
    "simple-gemini": calculate_simple_doc_confidence(request),
    "deep-gemini": calculate_deep_analysis_confidence(request),
    "plan-down": calculate_planning_confidence(request)
}

# Interactive Mode (Default)
if max(confidence_scores.values()) >= 60:
    selected_tool = max(confidence_scores, key=confidence_scores.get)
else:
    # Ambiguous - ask user for clarification
    ask_user_to_clarify()

# Full Automation Mode (if user requested)
if automation_mode_enabled:
    if max(confidence_scores.values()) >= 50:  # Lower threshold
        selected_tool = max(confidence_scores, key=confidence_scores.get)
        log_auto_decision(selected_tool, confidence_scores)
    else:
        # Fallback to Main Claude
        selected_tool = "main_claude"

Phase 3: Execution Strategy

Single Skill Execution:

User Request → Analyze → Match to Skill X → Invoke Skill X → Return Result

Multi-Skill Execution (Sequential):

Example: "Generate docs then check code"
1. Invoke simple-gemini (generate docs)
2. Wait for completion
3. Invoke codex-code-reviewer (check code)
4. Return combined results

Multi-Skill Execution (Parallel - if independent):

Example: "Generate plan and README simultaneously"
1. Invoke plan-down in parallel
2. Invoke simple-gemini in parallel
3. Wait for both to complete
4. Return combined results

Phase 4: Disambiguation

When multiple skills could apply:

Option 1: Ask User (Interactive Mode)

Detected that your request can use the following skills:

1. simple-gemini - Generate standard documentation
2. deep-gemini - Generate deep analysis documentation

Please choose:
- Enter 1: Use simple-gemini (fast, standardized)
- Enter 2: Use deep-gemini (in-depth, includes complexity analysis)

Option 2: Auto-Select (Full Automation Mode)

CRITICAL: In Full Automation Mode, DO NOT ask user "continue?" or present choices

Activation: User explicitly requests "full automation"/"complete automation"/"automated process" in initial request
Behavior: Router and Main Claude make ALL decisions without user intervention
Forbidden Actions:
- "Continue?" (Yes/No)
- "Please choose..." (Option 1/2/3)
- "Do you need..." (Need/Don't need)
- Any form of asking user for confirmation or choice
Correct Actions:
- "[Full Auto Mode] Detected planning needed, auto-invoking plan-down..."
- "[Full Auto Mode] Code generated, auto-invoking codex for quality check..."
- Direct execution with logged rationale
Decision Rules:
- Uses confidence scores with lower threshold (≥50 instead of ≥60)
- Prefer simpler skills for ambiguous cases:
  - simple-gemini over deep-gemini (unless "deep" mentioned)
  - zen-chat over zen-thinkdeep (unless "investigate" or "bug" mentioned)
  - Direct execution over complex skills when unclear
- Log all auto-decisions with rationale for transparency
- Standards compliance: Always follows CLAUDE.md rules
Exception - Only Ask User When:
- Blocking errors (environment missing, dependency errors)
- Security risks (sensitive data exposure, production operations)

Full Automation Mode Decision Template:

[Full Auto Mode - Auto Decision]
Detected: {task_description}
Auto-selected: {selected_tool}
Confidence: {confidence_score}%
Rationale: {rationale based on standards and intent}
Standards basis: {relevant CLAUDE.md rules}

Starting execution...

Router Workflow: Step-by-Step

Step 1: Receive User Request

Main Router's Action:

User: "Help me check the just-generated code"

Router Internal Analysis:
- Keywords detected: "check", "code"
- Intent: Code review
- Target: Recently generated code
- Expected output: Quality report + fixes

Step 2: Read Standards & Discover MCPs (CRITICAL)

Main Router's Action:

Part A: Standards Reading

Standards Reading:
a) Global CLAUDE.md (/home/vc/.claude/CLAUDE.md):
   - G1: Documentation First-Class Citizen - code changes must synchronize doc updates
   - G3: No Execution Permission Scenario - requires explicit user consent
   - Current phase: P3 (Execute Solution) - just completed code generation

b) Global CLAUDE.md (/home/vc/.claude/CLAUDE.md):
   - Code standards: Clear, readable
   - Quality threshold: Coverage ≥ 70%

c) Project CLAUDE.md (./CLAUDE.md): [If exists]
   - Project-specific rules

d) Project CLAUDE.md (./CLAUDE.md): [If exists]
   - Model-specific requirements

Standards-Based Decision:
- P3 phase → Code review recommended after code changes (CLAUDE.md requirement)
- G1 rule → Must check if documentation was updated
- User approval needed before fixes (G3)

Part B: MCP Capability Reference (No Pre-check)

MCP Assumptions:

zen-mcp:
  Status:  Assumed AVAILABLE (default)
  Tools: All 13 zen-mcp tools assumed ready
  Strategy: Optimistic routing - verify on actual invocation

User-Mentioned MCPs:
  Detection: Check if user explicitly mentioned MCP tools in request
  Example triggers: "use serena", "use unifuncs to search", "call mcp__serena__find_symbol"
  Status:  Assumed AVAILABLE (if mentioned by user)
  Strategy: Optimistic routing - honor user's explicit tool choice

Optional Enhancement MCPs:
  serena: Can be discovered on-demand for code intelligence
  unifuncs: Can be discovered on-demand for web capabilities
  Strategy: Lazy discovery - only if needed for enhancement

Routing Decision for codex-code-reviewer:
  Required: mcp__zen__codereview (assumed available )
  Enhancement: serena tools (optional, will discover if needed)
  User preference: None mentioned in this request

→ Decision: Proceed with codex-code-reviewer
  Rationale: zen-mcp assumed available, no blocking issues

Step 3: Analyze Intent

Main Router's Action:

Intent Classification:
- Primary Intent: Code review
- Secondary Intent: None
- Complexity: Standard (not deep analysis)
- Urgency: Normal

Context Signals:
- Git status shows modified files: src/features.py, src/model_training.py
- No explicit skill mentioned by user
- Recent activity: Code generation just completed

Standards Alignment:
- Matches P3 phase requirement for post-code-change review
- Aligns with G1 (need to verify doc updates)

Step 4: Match to Skill(s) (Optimistic Routing)

Main Router's Action:

Skill Matching:
- codex-code-reviewer: 95% confidence
  - Reason: Intent is code review, has modified files
  - Standards support: P3 phase requirement
  - zen-mcp: Assumed available (optimistic)
- simple-gemini: 10%
  - zen-mcp: Assumed available
- deep-gemini: 15%
  - zen-mcp: Assumed available
- plan-down: 5%
  - zen-mcp: Assumed available

Decision: Route to codex-code-reviewer
Rationale: Highest confidence + Standards alignment
Note: zen-mcp availability assumed, will verify during execution

Step 5: Invoke Selected Skill (Optimistic Execution)

Main Router's Action:

Invoking: codex-code-reviewer

Parameters:
- Files to review: [src/features.py, src/model_training.py]
- Review type: full
- User approval: required

MCP Strategy:
  Primary tools: zen-mcp (assumed available, no pre-check)
  Enhancement tools: serena/unifuncs (can discover on-demand if needed)

Execution:
[codex-code-reviewer executes workflow using zen-mcp tools]

Error Handling (if zen-mcp fails):
  1. Skill reports error to router
  2. Router notifies user: "mcp__zen__codereview currently unavailable"
  3. Router suggests fallback: Main Claude direct code review
  4. User chooses: Continue with fallback OR troubleshoot MCP

Step 6: Return Results to User

Main Router's Action:

Code review completed (using codex-code-reviewer):

Review results:
- Reviewed files: 2
- Issues found: 3 (fixed)
- Review rounds: 2 / 5

Standards compliance check:
 G1: Verified documentation updates (PROJECTWIKI.md, CHANGELOG.md)
 G3: User authorization obtained before fixes
 Quality threshold: Coverage reached 75% (exceeds 70% threshold)

Detailed report:
[codex-code-reviewer's output]

Routing Examples

Note: For detailed routing examples with comprehensive Chinese descriptions and step-by-step decision processes, please refer to: references/routing_examples.md

Quick Example Index

The routing_examples.md file contains 14 complete examples demonstrating main-router's decision-making process:

Example 1: General Q&A Request - Simple conceptual questions → zen-chat
Example 2: Deep Problem Investigation - Complex debugging → zen-thinkdeep
Example 3: Simple Document Generation - Standard docs → simple-gemini
Example 4: Code Quality Check - Explicit codex request → codex-code-reviewer
Example 5: Deep Technical Analysis - Complexity analysis → deep-gemini
Example 6: Planning Request - Task decomposition → plan-down
Example 7: Ambiguous Request - Clarification workflow
Example 8: Multi-Skill Sequential - Multi-task execution
Example 9: Full Automation Mode - Zero-wait principle demonstration
Example 10: User Explicitly Mentions MCP Tools - MCP tool routing
Example 11: Complete Task Lifecycle ⭐ BEST PRACTICE - Active monitoring
Example 12: Frontend Development Request ⭐ NEW - Frontend detection scoring → gemini-frontend
Example 13: Fullstack Project Detection ⭐ NEW - Task decomposition for fullstack
Example 14: Code + Review Workflow - Sequential skill invocation

Quick Reference - Example 1 (General Q&A):

User: "Explain what is overfitting in machine learning?"

Router Decision:

Intent: General Q&A
Keywords: "explain", "what is"
Target: Conceptual explanation
Output: Answer/explanation (no file operations)

→ Route to: zen-chat

Rationale:
- Pure conceptual question
- No file/code operations required
- Fast response with zen-chat is sufficient
- No need for complex analysis workflow

Best Practices

For Effective Routing

Active Monitoring (CRITICAL - Anti-Lazy Principle):
- Router MUST monitor task lifecycle continuously
- Proactively invoke skills at appropriate stages
- NEVER allow Main Claude to skip quality checks
- Mandatory interventions:
  - plan.md generation → MUST use plan-down
  - Code generation complete → MUST use codex-code-reviewer
  - Test code needed → MUST use simple-gemini → codex validation
  - Documentation needed → MUST use simple-gemini/deep-gemini
- Think: "What skill should be used at this stage?"
Keyword Detection:
- Look for explicit skill/tool names (chat, thinkdeep, codex, gemini, planner)
- Look for action verbs (explain, investigate, check, generate, analyze, plan)
- Look for output types (answer, investigation report, documentation, plan, test)
- Look for question patterns (what is, how to understand, why)
Context Awareness:
- Check git status for recently modified files
- Check for existing artifacts (plan.md, PROJECTWIKI.md)
- Consider user's recent interactions
- Note project phase (planning, development, review)
Confidence Thresholds:
- High confidence (≥80): Auto-route
- Medium confidence (60-79): Auto-route with notification
- Low confidence (<60): Ask user for clarification
User Communication:
- Always inform user which skill was selected
- Provide rationale for skill selection
- Allow user to override router's decision
Error Handling:
- If selected skill fails, offer fallback options
- If no skill matches, execute directly with Main Claude
- If user request is unclear, ask clarifying questions

Router Communication Template

Format:

[Decision Notification]
Detected task type: [Task Type]
Selected skill: [Skill Name]
Rationale: [Brief explanation]

Starting execution...

Example:

[Decision Notification]
Detected task type: Code quality review
Selected skill: codex-code-reviewer
Rationale: You requested code quality check, codex-code-reviewer provides comprehensive 5-dimensional review

Starting execution...

Routing Decision Matrix

User Intent	Primary Keywords	Selected Tool/Skill	Rationale
General Q&A	explain, what is, how to understand	zen-chat	General Q&A, no file ops
Deep Problem Investigation	deeply analyze problem, investigate bug, systematic analysis	zen-thinkdeep	Multi-stage investigation
Code Review	check, review, codex	codex-code-reviewer	Code quality validation
Standard Documentation	documentation, README, CHANGELOG, test	simple-gemini	Standard doc templates
Deep Technical Analysis	deep, analyze, architecture, performance, complexity	deep-gemini	Technical analysis + complexity
Planning	plan, planning, decompose	plan-down	Task decomposition + validation
Document Generation (Unclear)	generate document	Ask User	Ambiguous - need clarification

Special Cases

Case 1: No Matching Skill

Scenario: User request doesn't match any skill

Action:

Router Analysis:
- No skill confidence > 60%
- Request is outside skill scope

→ Decision: Execute directly with Main Claude
→ Notification: "This task will be handled directly by the main model (no specialized skill needed)"

Case 2: Conflicting Skills

Scenario: Multiple skills have similar confidence scores

Action:

Router Analysis:
- simple-gemini: 75%
- deep-gemini: 73%
- Difference < 10% → Ambiguous

→ Decision: Ask user to choose
→ Present both options with pros/cons

Case 3: Runtime MCP Tool Failure

Scenario A: zen-mcp tool fails during skill execution (discovered at runtime)

Action:

Skill Execution Error:
- Skill: deep-gemini
- Failed MCP call: mcp__zen__docgen
- Error: "MCP tool not available" or "Connection failed"

Router Receives Error and Responds:

→ Notification to User:
  "Issue encountered while executing deep-gemini:
   mcp__zen__docgen is currently unavailable.

   Available options:
   1. Use simple-gemini (only requires mcp__zen__clink)
   2. Main model generates document directly (no MCP enhancement)
   3. Check zen-mcp service status and retry

   Please choose (or enter 3 and use /mcp status to check)"

User Choice Handling:
- Choice 1 → Route to simple-gemini
- Choice 2 → Main Claude direct execution
- Choice 3 → Wait for user to troubleshoot, then retry

Note: This only happens when zen-mcp actually fails at runtime,
      not during routing phase (optimistic assumption).

Scenario B: User-mentioned MCP tool fails at runtime

Action:

Direct MCP Invocation Error:
- User request: "Use serena's find_symbol to analyze code"
- Failed MCP call: mcp__serena__find_symbol
- Error: "MCP server 'serena' not found" or "Tool not available"

Router Receives Error and Responds:

→ Notification to User:
  "Your specified MCP tool is currently unavailable:
   mcp__serena__find_symbol

   Error details: {error_details}

   Available options:
   1. Use zen-mcp's code analysis tool (mcp__zen__thinkdeep)
   2. Main model reads code directly for analysis
   3. Check serena MCP service status and retry (/mcp status)

   Please choose handling method:"

User Choice Handling:
- Choice 1 → Route to zen-thinkdeep (alternative analysis)
- Choice 2 → Main Claude manual code reading
- Choice 3 → Wait for user to troubleshoot, then retry original request

Note: User-mentioned MCP tools are assumed available (optimistic),
      but must provide clear error feedback if they fail at runtime.

Case 4: User Override

Scenario: User explicitly requests a different skill

User: "Don't use codex, use gemini to analyze"

Action:

Router Analysis:
- Original selection: codex-code-reviewer
- User override: Use gemini (deep-gemini)

→ Decision: Respect user choice
→ Route to: deep-gemini
→ Notification: "Switched to deep-gemini (as per your request)"

Notes

Core Principles

Active Task Monitoring (HIGHEST PRIORITY - Anti-Lazy Principle):
- Router MUST continuously monitor task lifecycle
- Proactively invoke skills at critical stages
- Mandatory skill usage rules (NEVER skip):
  - plan.md → plan-down (MANDATORY)
  - Code complete → codex-code-reviewer (MANDATORY)
  - Test code → simple-gemini + codex validation (MANDATORY)
  - Documentation → simple-gemini or deep-gemini (MANDATORY)
- Think at each stage: "Should I invoke a skill here?"
- Being lazy is FORBIDDEN - always use proper skills
Standards-First Approach: ALWAYS read CLAUDE.md before routing decisions
- Global CLAUDE.md: /home/vc/.claude/CLAUDE.md
- Global CLAUDE.md: /home/vc/.claude/CLAUDE.md
- Project CLAUDE.md: ./CLAUDE.md (if exists)
- Project CLAUDE.md: ./CLAUDE.md (if exists)
MCP-Aware Routing: Optimistic assumption with lazy verification
- zen-mcp assumed available by default - No pre-check required
- User-mentioned MCP tools assumed available - Honor user's explicit tool choice
- Route to skills immediately without MCP verification
- Verify MCP availability lazily (on actual tool invocation)
- Only communicate with user if MCP tools fail at runtime
- Optional: Discover serena/unifuncs MCPs on-demand for enhancements
- Provide fallback options when MCP tools actually fail
Proactive Usage: Main Router should be invoked for ALL task-related user requests
Standards Compliance: All routing decisions must align with documented rules
- Respect phase requirements (P1→P2→P3)
- Follow global rules (G1-G8)
- Apply project-specific overrides when applicable

Operation Modes

Interactive Mode (Default):
- Ask user for clarification when ambiguous
- Confidence threshold: ≥60%
- User makes final decisions
- Provide recommendations with rationale
Full Automation Mode:
- Activation: Keywords in user's initial request: "full automation", "complete automation", "automated workflow"
- Behavior: Router and Main Claude make ALL decisions autonomously
- CRITICAL - DO NOT ask user:
  - "Should I continue?"
  - "Please choose..."
  - "Do you need..."
  - Direct execution with logged rationale
- Decision Rules:
  - Lower confidence threshold: ≥50%
  - Auto-select best option (no user choice)
  - Log all auto-decisions with rationale
  - Standards-based decision making (no guessing)
- Exception: Only ask when blocking errors or security risks occur
- Mandatory Final Step: After all tasks complete, generate auto_log.md using simple-gemini
  - Purpose: Complete transparency and audit trail
  - Content: Decision timeline, skills invoked, rationale, results
  - Format: Structured Markdown with timestamps and decision tree
- Prefer simpler skills when ambiguous

Best Practices

Transparency: Always inform user which skill/tool was selected and why
- Focus on intent match and standards alignment in routing notification
- Honor user's explicit MCP tool choice (e.g., "use serena") without pre-checking
- Only mention MCP status if there's a runtime failure
- Acknowledge when routing based on user's explicit MCP preference
Flexibility: Support user overrides and manual skill selection
- Allow user to choose alternative skills if zen-mcp fails at runtime
- Respect user's explicit skill preference
Efficiency: Prefer simpler skills when ambiguous
- simple-gemini over deep-gemini (unless "deep" mentioned)
- zen-chat over zen-thinkdeep (unless "investigate" or "bug" mentioned)
- Direct execution over complex skills when unclear
Context-Aware: Consider project state, recent activity, git status, and CLAUDE.md phase
- Leverage serena memory tools for project context (if discovered)
- Use git status to inform routing decisions
Multi-Skill Support: Handle sequential and parallel skill execution
- Route to multiple skills when user requests multi-task workflows
- Execute skills independently or sequentially as needed
Fallback Strategy: Graceful degradation on runtime failures
- zen-mcp fails → Notify user and suggest alternative skill or Main Claude
- Provide actionable troubleshooting steps (e.g., "/mcp status")
- Allow user to retry after troubleshooting
Continuous Improvement: Learn from user corrections and overrides
- Track skill selection patterns for future optimization
- Log runtime MCP failures for debugging
No Redundancy: Don't invoke router for meta-requests about the router itself

Router Self-Awareness

The router should NOT route these requests:

"What skills are available?" → Direct answer
"How does routing work?" → Direct answer
"Explain the router" → Direct answer
General questions about Claude Code or skills → Direct answer

The router SHOULD route these requests:

General Q&A and explanations → zen-chat
Deep problem investigation → zen-thinkdeep
Any task-specific request (code review, docs, planning, analysis)
Requests with explicit skill/tool names
Requests with clear intent (review, generate, analyze, plan, explain, investigate)

Install Skill

SKILL.md

Main Router - Intelligent Skill Routing Scheduler

Overview

When to Use This Skill

Available Skills Registry

0. zen-chat (Direct Tool)

0.5. zen-thinkdeep (Direct Tool)

1. codex-code-reviewer

2. simple-gemini

3. deep-gemini

4. plan-down ⭐ MANDATORY for Planning

5. gemini-frontend ⭐ MANDATORY for Frontend/Mobile Development

Routing Decision Logic

Phase 0: Standards Reading & MCP Discovery (CRITICAL - First Step)

Phase 0.1: Read Standards

Phase 0.2: 动态 MCP 发现与智能适配（Dynamic MCP Discovery & Adaptation）

MCP 环境扫描策略

技能 → MCP 依赖关系（动态映射）

智能适配与降级策略

运行时适配示例

MCP 可用性缓存与刷新

Standards-Based Routing Rules（基于标准的路由规则）

Phase 0.3: Set Coverage Target (G9 Compliance)

Fixed Routing Rules (MANDATORY - Auto-Trigger)

Phase 1: Intent Classification

Phase 2: Skill Matching

Phase 3: Execution Strategy

Phase 4: Disambiguation

Router Workflow: Step-by-Step

Step 1: Receive User Request

Step 2: Read Standards & Discover MCPs (CRITICAL)

Step 3: Analyze Intent

Step 4: Match to Skill(s) (Optimistic Routing)

Step 5: Invoke Selected Skill (Optimistic Execution)

Step 6: Return Results to User

Routing Examples

Quick Example Index

Best Practices

For Effective Routing

Router Communication Template

Routing Decision Matrix

Special Cases

Case 1: No Matching Skill

Case 2: Conflicting Skills

Case 3: Runtime MCP Tool Failure

Case 4: User Override

Notes

Core Principles

Operation Modes

Best Practices

Router Self-Awareness