Claude-Code-Workflow/docs/guide/ch05-advanced-tips.md

Advanced Tips

One-Line Positioning

Drive AI tool orchestration with natural language — Semantic CLI invocation, multi-model collaboration, intelligent memory management.

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5.1 Semantic Tool Orchestration

5.1.1 Core Concept

CCW's CLI tools are AI-automated capability extensions. Users simply describe needs in natural language, and AI automatically selects and invokes the appropriate tools.

::: tip Key Understanding

• User says: "Use Gemini to analyze this code"
• AI automatically: Invokes Gemini CLI + applies analysis rules + returns results
• Users don't need to know ccw cli command details :::

5.1.2 Available Tools & Capabilities

Tool	Strengths	Typical Trigger Words
Gemini	Deep analysis, architecture design, bug diagnosis	"use Gemini", "deep understanding"
Qwen	Code generation, feature implementation	"let Qwen implement", "code generation"
Codex	Code review, Git operations	"use Codex", "code review"
OpenCode	Open-source multi-model	"use OpenCode"

5.1.3 Semantic Trigger Examples

Simply express naturally in conversation, AI will automatically invoke the corresponding tool:

Goal	User Semantic Description	AI Auto-Executes
Security Assessment	"Use Gemini to scan auth module for security vulnerabilities"	Gemini + Security analysis rule
Code Implementation	"Let Qwen implement a rate limiting middleware"	Qwen + Feature implementation rule
Code Review	"Use Codex to review this PR's changes"	Codex + Review rule
Bug Diagnosis	"Use Gemini to analyze the root cause of this memory leak"	Gemini + Diagnosis rule

5.1.4 Underlying Configuration (Optional)

AI tool invocation configuration file at ~/.claude/cli-tools.json:

{
  "tools": {
    "gemini": {
      "enabled": true,
      "primaryModel": "gemini-2.5-flash",
      "tags": ["analysis", "Debug"]
    },
    "qwen": {
      "enabled": true,
      "primaryModel": "coder-model",
      "tags": ["implementation"]
    }
  }
}

::: info Note Tags help AI automatically select the most suitable tool based on task type. Users typically don't need to modify this configuration. :::

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5.2 Multi-Model Collaboration

5.2.1 Collaboration Patterns

Through semantic descriptions, multiple AI models can work together:

Pattern	Description Style	Use Case
Collaborative	"Let Gemini and Codex jointly analyze architecture issues"	Multi-perspective analysis of the same problem
Pipeline	"Gemini designs, Qwen implements, Codex reviews"	Stage-by-stage complex task completion
Iterative	"Use Gemini to diagnose, Codex to fix, iterate until tests pass"	Bug fix loop
Parallel	"Let Gemini and Qwen each provide optimization suggestions"	Compare different approaches

5.2.2 Semantic Examples

Collaborative Analysis

User: Let Gemini and Codex jointly analyze security and performance of src/auth module
AI: [Automatically invokes both models, synthesizes analysis results]

Pipeline Development

User: I need to implement a WebSocket real-time notification feature.
      Please have Gemini design the architecture, Qwen implement the code, and Codex review.
AI: [Sequentially invokes three models, completing design→implement→review flow]

Iterative Fix

User: Tests failed. Use Gemini to diagnose the issue, have Qwen fix it, loop until tests pass.
AI: [Automatically iterates diagnose-fix loop until problem is resolved]

5.2.3 Model Selection Guide

Task Type	Recommended Model	Reason
Architecture Analysis	Gemini	Strong at deep understanding and pattern recognition
Bug Diagnosis	Gemini	Powerful root cause analysis capability
Code Generation	Qwen	High code generation efficiency
Code Review	Codex	Good Git integration, standard review format
Long Text Processing	Claude	Large context window

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5.3 Intelligent Memory Management

5.3.1 Memory System Overview

CCW's memory system is an AI self-managed knowledge base, including:

Category	Purpose	Example
learnings	Learning insights	New technology usage experience, best practices
decisions	Architecture decisions	Technology selection rationale, design tradeoffs
conventions	Coding standards	Naming conventions, code style
issues	Known issues	Bug records, limitations

5.3.2 Automatic Memory Usage

AI automatically retrieves and applies relevant memories when executing tasks:

User: Help me implement the user authentication module
AI: [Automatically retrieves authentication-related decisions and conventions from memory]
    Based on previous technical decisions, we use JWT + bcrypt approach...

5.3.3 How Users Guide Memory

While AI manages memory automatically, users can actively reinforce:

Explicitly Request to Remember

User: Remember this naming convention: all API routes use kebab-case
AI: [Stores this convention in conventions memory]

Request to Review Decisions

User: Why did we choose Redis for caching before?
AI: [Retrieves from decisions memory and responds]

Correct Wrong Memory

User: The previous decision changed, we now use PostgreSQL instead of MongoDB
AI: [Updates related decision memory]

5.3.4 Memory File Locations

• Global Memory: ~/.claude/projects/{project-name}/memory/
• Project Memory: .claude/memory/ or MEMORY.md

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5.4 Hook Automation

5.4.1 Hook Concept

Hooks are automated processes before and after AI executes tasks, users don't need to trigger manually:

Hook Type	Trigger Time	Purpose
pre-command	Before AI thinking	Load project specs, retrieve memory
post-command	After AI completion	Save decisions, update index
pre-commit	Before Git commit	Code review, standard checks

5.4.2 Configuration Example

Configure in .claude/hooks.json:

{
  "pre-command": [
    {
      "name": "load-project-specs",
      "description": "Load project specifications",
      "command": "cat .workflow/specs/project-constraints.md"
    }
  ],
  "post-command": [
    {
      "name": "save-decisions",
      "description": "Save important decisions",
      "command": "ccw memory import \"{content}\""
    }
  ]
}

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5.5 ACE Semantic Search

5.5.1 What is ACE

ACE (Augment Context Engine) is AI's code perception capability, enabling AI to understand the entire codebase semantically.

5.5.2 How AI Uses ACE

When users ask questions, AI automatically uses ACE to search for relevant code:

User: How is the authentication flow implemented?
AI: [Uses ACE semantic search for auth-related code]
    Based on code analysis, the authentication flow is...

5.5.3 Configuration Reference

Configuration Method	Link
Official Docs	Augment MCP Documentation
Proxy Tool	ace-tool (GitHub)

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5.6 Semantic Prompt Cheatsheet

Common Semantic Patterns

Goal	Semantic Description Example
Analyze Code	"Use Gemini to analyze the architecture design of src/auth"
Security Audit	"Use Gemini to scan for security vulnerabilities, focus on OWASP Top 10"
Implement Feature	"Let Qwen implement a cached user repository"
Code Review	"Use Codex to review recent changes"
Bug Diagnosis	"Use Gemini to analyze the root cause of this memory leak"
Multi-Model Collaboration	"Gemini designs, Qwen implements, Codex reviews"
Remember Convention	"Remember: all APIs use RESTful style"
Review Decision	"Why did we choose this tech stack before?"

Collaboration Pattern Cheatsheet

Pattern	Semantic Example
Collaborative	"Let Gemini and Codex jointly analyze..."
Pipeline	"Gemini designs, Qwen implements, Codex reviews"
Iterative	"Diagnose and fix until tests pass"
Parallel	"Let multiple models each provide suggestions"

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Next Steps

• Best Practices — Team collaboration standards, code review process, documentation maintenance strategy