Class Seven

Multi-agent development team workflow skill. Use when coordinating complex development tasks requiring multiple specialized roles - PM, Architect, Developer, Tester, Debugger. Orchestrates sub-agents as a development team with main session as manager. Supports flexible tool selection (Claude Code, Kimi, native tools) based on task characteristics.

Class Seven - Multi-Agent Development Team

Class Seven (七班) is a structured multi-agent workflow that treats sub-agents as specialized development team members.

When to Use

Use this skill when:

• Complex development tasks requiring multiple perspectives/roles
• Tasks needing PM planning + architecture + implementation + testing
• Debugging scenarios requiring systematic investigation
• Code review and quality assurance workflows
• Projects requiring end-to-end delivery (plan → build → test → deploy)

Team Structure

Main Session (Manager)
├── PM Agent (产品经理)
├── Architect Agent (架构师)
├── Developer Agent (开发工程师)
├── Tester Agent (测试工程师)
└── Debugger Agent (调试专家)

Workflow Phases

Phase 1: Task Analysis & Planning

1. Manager (Main Session) analyzes task complexity
2. Spawn PM Agent for requirement clarification
3. PM returns: requirements doc, scope, acceptance criteria

Phase 2: Architecture & Design

1. Spawn Architect Agent with PM output
2. Architect returns: tech stack, module design, interfaces

Phase 3: Implementation

1. Spawn Developer Agent with architecture specs
2. Developer returns: implemented code

Phase 4: Quality Assurance

1. Spawn Tester Agent with code + requirements
2. Tester returns: test plan, test cases, bugs found

Phase 5: Debugging (if needed)

1. Spawn Debugger Agent with bug reports
2. Debugger returns: root cause analysis, fixes

Phase 6: Integration & Delivery

1. Manager reviews all outputs
2. Integrates final deliverable
3. Validates against acceptance criteria

Tool Selection Matrix

Task Type	Primary Tool	Secondary Tool	Reason
Complex architecture	Claude Code	Kimi	Deep reasoning, context management
Quick prototyping	Kimi	Native	Fast iteration, lower latency
Deep debugging	Claude Code	Kimi	Multi-file analysis, bug tracing
Code review	Kimi	Claude Code	Pattern recognition, best practices
Testing	Native	Kimi	Deterministic execution
Documentation	Kimi	Native	Chinese/English bilingual

Agent Personas

PM Agent

Role: 产品经理
Expertise: Requirements analysis, user stories, acceptance criteria
Output: PRD, user stories, scope definition
Tools: Kimi (for Chinese context), Claude Code (for complex products)

Architect Agent

Role: 架构师
Expertise: System design, tech stack selection, API design
Output: Architecture doc, module diagrams, interface specs
Tools: Claude Code (preferred for architecture), Kimi (for validation)

Developer Agent

Role: 开发工程师
Expertise: Code implementation, refactoring, optimization
Output: Production-ready code
Tools: Claude Code (complex logic), Kimi (quick implementation), Native (boilerplate)

Tester Agent

Role: 测试工程师
Expertise: Test design, edge case identification, quality assurance
Output: Test cases, test scripts, bug reports
Tools: Native (execution), Kimi (test design), Claude Code (complex scenarios)

Debugger Agent

Role: 调试专家
Expertise: Root cause analysis, performance profiling, bug fixing
Output: RCA report, patches, prevention recommendations
Tools: Claude Code (deep analysis), Kimi (pattern matching)

Execution Modes

Mode A: Full Team (Full Orchestration)

All 5 phases executed sequentially. Use for complex projects.

Mode B: Sprint Team (Dev + Test)

Skip PM/Architect phases. Use when requirements are clear.

Mode C: Firefighter (Debug Only)

Debugger agent only. Use for urgent bug fixes.

Mode D: Review Board (PM + Tester)

Code review workflow. Use for quality gates.

Quick Commands

# Full team deployment
class_seven deploy --mode=full --task="<description>"

# Sprint mode
class_seven deploy --mode=sprint --specs="<requirements>"

# Debug mode
class_seven deploy --mode=debug --bug="<bug description>"

Best Practices

1. Always start with task analysis - Determine mode and required agents
2. Pass context explicitly - Each agent receives relevant previous outputs
3. Set clear boundaries - Define what each agent should/shouldn't do
4. Use appropriate timeout - Complex tasks need longer timeouts
5. Review before integration - Manager validates all outputs

Error Handling

If an agent fails or produces insufficient output:

1. Analyze failure reason
2. Respawn with clearer instructions or different tool
3. Consider breaking task into smaller sub-tasks
4. Escalate to human if stuck after 2 retries

References

• Detailed workflow patterns: See references/workflows.md
• Tool integration guide: See references/tools-guide.md
• Example projects: See references/examples.md