Implemented comprehensive CQRS pattern for Sprint module: Commands: - UpdateSprintCommand: Update sprint details with validation - DeleteSprintCommand: Delete sprints (business rule: cannot delete active sprints) - StartSprintCommand: Transition sprint from Planned to Active - CompleteSprintCommand: Transition sprint from Active to Completed - AddTaskToSprintCommand: Add tasks to sprint with validation - RemoveTaskFromSprintCommand: Remove tasks from sprint Queries: - GetSprintByIdQuery: Get sprint by ID with DTO mapping - GetSprintsByProjectIdQuery: Get all sprints for a project - GetActiveSprintsQuery: Get all active sprints across projects Infrastructure: - Created IApplicationDbContext interface for Application layer DB access - Registered IApplicationDbContext in DI container - Added Microsoft.EntityFrameworkCore package to Application layer - Updated UnitOfWork to expose GetDbContext() method API: - Created SprintsController with all CRUD and lifecycle endpoints - Implemented proper HTTP methods (POST, PUT, DELETE, GET) - Added sprint status transition endpoints (start, complete) - Added task management endpoints (add/remove tasks) All tests passing. Ready for Tasks 4-6. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
ColaFlow Agent System
This directory contains the sub agent configurations for the ColaFlow project.
📚 Documentation Index
| Document | Purpose |
|---|---|
| README.md (this file) | Overview and quick start |
| AGENT_CONFIGURATION_GUIDE.md | Complete agent configuration guide |
| AGENT_QUICK_REFERENCE.md | Quick reference for agent setup |
| RESEARCH_REPORT_AGENT_CONFIGURATION.md | Research findings and technical details |
| verify-agents.md | Agent configuration validation checklist |
| USAGE_EXAMPLES.md | Detailed usage examples |
Structure
.claude/
├── agents/ # Sub agent configurations
│ ├── researcher.md # Technical Researcher agent
│ ├── product-manager.md # Product Manager agent
│ ├── architect.md # System Architect agent
│ ├── backend.md # Backend Engineer agent
│ ├── frontend.md # Frontend Engineer agent
│ ├── ai.md # AI Engineer agent
│ ├── qa.md # QA Engineer agent
│ ├── ux-ui.md # UX/UI Designer agent
│ └── progress-recorder.md # Progress Recorder agent
├── skills/ # Skills for quality assurance
│ └── code-reviewer.md # Code review and standards enforcement
├── AGENT_CONFIGURATION_GUIDE.md # ⭐ Complete configuration guide
├── AGENT_QUICK_REFERENCE.md # ⭐ Quick reference card
├── RESEARCH_REPORT_AGENT_CONFIGURATION.md # ⭐ Technical research report
├── verify-agents.md # ⭐ Validation checklist
├── USAGE_EXAMPLES.md # Detailed usage examples
└── README.md # This file
../CLAUDE.md # Main coordinator (project root)
⚡ Quick Start
For Users
-
Verify Agent Configuration
# Check if agents are properly configured ls .claude/agents/See verify-agents.md for detailed validation.
-
Use Agents via Main Coordinator Simply talk to Claude - it will automatically route tasks to the right agent:
请研究 NestJS 最佳实践 → researcher agent 实现用户登录 API → backend agent 设计看板界面 → ux-ui + frontend agents -
Explicitly Call an Agent (optional)
请使用 researcher agent 查找最新的 React 文档
For Developers
New to Claude Code agents? Start with:
- Read AGENT_QUICK_REFERENCE.md (5 min)
- Review AGENT_CONFIGURATION_GUIDE.md (comprehensive)
- Run validation: verify-agents.md
Configuring a new agent? Use this template:
---
name: your-agent-name
description: Clear description of agent's purpose and when to invoke it
tools: Read, Write, Edit, Bash, TodoWrite
model: inherit
---
# Your Agent
Agent's system prompt content...
Agent Roles
| Agent | File | Responsibilities |
|---|---|---|
| Main Coordinator | CLAUDE.md |
Understands requirements, routes tasks to appropriate agents, integrates results |
| Researcher | agents/researcher.md |
Technical research, API documentation, best practices |
| Product Manager | agents/product-manager.md |
Project planning, requirements management, progress tracking |
| Architect | agents/architect.md |
System architecture, technology selection, scalability |
| Backend Engineer | agents/backend.md |
Server-side code, API design, database, MCP integration |
| Frontend Engineer | agents/frontend.md |
UI development, components, state management |
| AI Engineer | agents/ai.md |
AI features, prompt engineering, model integration |
| QA Engineer | agents/qa.md |
Test strategy, test cases, quality assurance |
| UX/UI Designer | agents/ux-ui.md |
User experience, interface design, design system |
| Progress Recorder | agents/progress-recorder.md |
Project memory management, progress tracking, information archiving |
Skills
Skills are quality assurance mechanisms that automatically apply to agent outputs:
| Skill | File | Purpose |
|---|---|---|
| Code Reviewer | skills/code-reviewer.md |
Ensures all code follows proper coding standards, best practices, and maintains high quality |
How Skills Work
Skills are automatically applied by the main coordinator when:
- Backend or Frontend agents generate code
- Any code modifications are proposed
- Code refactoring is performed
The Code Reviewer skill checks for:
- ✅ Naming conventions (camelCase, PascalCase, etc.)
- ✅ TypeScript best practices
- ✅ Error handling patterns
- ✅ Security vulnerabilities
- ✅ Performance considerations
- ✅ Common anti-patterns
If issues are found, the coordinator will request fixes before presenting the code to you.
How It Works
1. Main Coordinator Routes Tasks
The main coordinator (defined in CLAUDE.md at project root) receives all user requests and routes them to appropriate sub agents using the Task tool.
Example:
User: "I need to implement the MCP Server"
Main Coordinator analyzes the request and determines:
- Needs architecture design
- Needs backend implementation
- Needs testing strategy
Main Coordinator calls:
1. Task tool with subagent_type="architect"
2. Task tool with subagent_type="backend"
3. Task tool with subagent_type="qa"
2. Sub Agents Execute Tasks
Each sub agent is specialized in their domain and produces high-quality, domain-specific outputs:
- Product Manager: PRD documents, project plans, progress reports
- Architect: Architecture designs, technology recommendations
- Backend: Clean, tested backend code
- Frontend: Beautiful, performant UI components
- AI: AI features with safety mechanisms
- QA: Comprehensive test cases and test strategies
- UX/UI: User-friendly interface designs
3. Main Coordinator Integrates Results
The main coordinator collects outputs from all sub agents and presents a unified response to the user.
Usage Examples
Example 1: Implement New Feature
User Request: "Implement AI-powered task creation feature"
Main Coordinator Flow:
- Calls
architectagent → Get technical architecture - Calls
product-manageragent → Define requirements and acceptance criteria - Calls
aiagent → Design prompts and model integration - Calls
backendagent → Implement API and MCP Server - Calls
frontendagent → Build UI and AI console - Calls
qaagent → Create test cases - Integrates all results and reports to user
Example 2: Fix Performance Issue
User Request: "Kanban board loads slowly with many tasks"
Main Coordinator Flow:
- Calls
qaagent → Performance testing and profiling - Based on findings, calls
frontendagent → Optimize rendering - Or calls
backendagent → Optimize API queries - Calls
qaagent again → Verify performance improvement
Example 3: Design New UI
User Request: "Design the sprint planning interface"
Main Coordinator Flow:
- Calls
product-manageragent → Define sprint planning requirements - Calls
ux-uiagent → Design user flows and mockups - Calls
frontendagent → Implement the design - Calls
qaagent → Usability testing
Calling Sub Agents
Sub agents are called using the Task tool with the subagent_type parameter:
Task({
subagent_type: "architect", // or "product-manager", "backend", etc.
description: "Short task description",
prompt: "Detailed instructions for the agent..."
})
Parallel Execution
For independent tasks, you can call multiple agents in parallel by using multiple Task calls in a single message:
// Single message with multiple Task calls
Task({ subagent_type: "architect", ... })
Task({ subagent_type: "product-manager", ... })
Sequential Execution
For dependent tasks, call agents sequentially (wait for first agent's response before calling the next).
Best Practices
- Clear Instructions: Provide detailed, specific prompts to sub agents
- Right Agent: Route tasks to the most appropriate agent
- Context: Include relevant project context (see
product.md) - Integration: Integrate results before presenting to user
- Parallel Work: Use parallel execution for independent tasks
Agent Collaboration
Agents suggest when other agents should be involved:
- Product Manager needs technical feasibility → Suggests calling Architect
- Backend needs API contract → Suggests calling Frontend
- Frontend needs design specs → Suggests calling UX/UI
- Any agent needs testing → Suggests calling QA
The main coordinator handles these routing decisions.
Project Context
All agents have access to:
product.md: Complete ColaFlow project planCLAUDE.md: Main coordinator guidelines.claude/agents/*.md: Other agent configurations
Quality Standards
Each agent follows strict quality standards:
- Code Quality: Clean, maintainable, well-tested code
- Documentation: Clear documentation and comments
- Best Practices: Industry best practices and standards
- Testing: Comprehensive test coverage
- Security: Security-first approach (especially for AI operations)
Getting Started
- Read
CLAUDE.mdin the project root to understand the main coordinator - Review
product.mdto understand the ColaFlow project - Check individual agent files in
.claude/agents/to understand each role - Start by asking the main coordinator (not individual agents directly)
Support
For questions about the agent system, refer to:
- Main coordinator:
CLAUDE.md - Project details:
product.md - Agent specifics:
.claude/agents/[agent-name].md