多智能体组织专家

---
name: agent-organization-expert  
description: 多智能体协同编排技能，用于团队组建、任务分解、工作流优化和协调策略，以实现最优的团队表现和资源利用。  
---  

# Agent Organization  

通过系统的任务分析、能力匹配和工作流设计，组建并协调多智能体团队。  

## Configuration  

- **Agent Count**: {{agent_count}}  
- **Task Type**: {{task_type}}  
- **Orchestration Pattern**: {{orchestration_pattern}}  
- **Max Concurrency**: {{max_concurrency}}  
- **Timeout (seconds)**: ${timeout_seconds:300}  
- **Retry Count**: ${retry_count:3}  

## Core Process  

1. **Analyze Requirements**: 理解任务范围、约束条件和成功标准  
2. **Map Capabilities**: 将可用智能体与所需技能相匹配  
3. **Design Workflow**: 创建包含依赖关系和检查点的执行计划  
4. **Orchestrate Execution**: 协调 {{agent_count}} 个智能体并监控进度  
5. **Optimize Continuously**: 基于性能反馈持续优化  

## Task Decomposition  

### Requirement Analysis  
- 将复杂任务拆分为独立的子任务  
- 识别每个子任务的输入/输出需求  
- 估算各组件的复杂性和资源需求  
- 为每个单元定义明确的成功标准  

### Dependency Mapping  
- 记录任务执行顺序的约束  
- 识别子任务之间的数据依赖  
- 映射资源共享需求  
- 检测潜在的瓶颈和冲突  

### Timeline Planning  
- 按依赖关系对任务排序  
- 识别可并行化的任务（最多 {{max_concurrency}} 个并发）  
- 为高风险组件分配缓冲时间  
- 定义用于进度验证的检查点
- **基于队列**：用于解耦任务的异步处理  
- **事件驱动**：对状态变化做出响应  

### 同步  
- 为依赖任务定义同步点  
- 实现带超时机制的等待（${timeout_seconds:300} 秒）  
- 妥善处理乱序完成的情况  
- 在各代理之间保持一致的状态  

### 冲突解决  
- 建立资源争用时的优先级规则  
- 定义冲突仲裁机制  
- 记录死锁情况下的回滚流程  
- 通过精心调度预防冲突  

## 性能优化  

### 负载均衡  
- 根据代理能力分配工作  
- 监控使用率并动态重新平衡  
- 避免过度负载高性能代理  
- 对数据密集型任务考虑代理的数据本地性  

### 瓶颈管理  
- 通过监控识别缓慢阶段  
- 为受限资源增加容量  
- 重构工作流以减少依赖  
- 在有益时缓存中间结果  

### 资源效率  
- 在代理间共享资源池  
- 使用后及时释放资源  
- 批量处理相似操作以降低开销  
- 监控并预警资源浪费  

## 监控与自适应  

### 进度跟踪  
- 按任务监控完成状态  
- 跟踪实际耗时与预估对比  
- 识别存在延迟风险的任务  
- 向利益相关者报告聚合进度  

### 性能指标  
- 任务完成率与延迟  
- 代理利用率与吞吐量  
- 错误率与恢复时间  
- 资源消耗与成本  

### 动态调整  
- 根据进度重新分配代理  
- 根据阻塞情况调整优先级  
- 根据工作负载扩展或缩减团队规模  
- 基于经验学习修改工作流  

## 错误处理  

### 故障检测  
- 监控任务失败和超时（${timeout_seconds:300} 秒阈值）  
- 及时检测代理不可用  
- 识别级联故障模式  
- 对异常行为发出警报  

### 恢复程序  
- 对瞬时故障进行退避重试（最多 ${retry_count:3} 次）  
- 必要时故障转移至备用代理  
- 在关键故障时回滚到最后检查点  
- 上报无法恢复的问题  

### 预防措施  
- 执行前验证输入  
- 分配前测试代理可用性  
- 设计支持优雅降级  
- 在关键路径中构建冗余  

## 质量保证  

### 验证关卡  
- 在每个检查点验证输出  
- 交叉核对并行任务的结果  
- 验证最终聚合结果  
- 确认已满足成功标准  

### 性能标准  
- 代理选择准确率目标：>${agent_selection_accuracy:95}%  
- 任务完成率目标：>${task_completion_rate:99}%  
- 响应时间目标：<${response_time_threshold:5} 秒  
- 资源利用率：最佳范围 ${utilization_min:60}-${utilization_max:80}%  

## 最佳实践  

### 规划  
- 投入时间进行彻底的任务分析  
- 记录假设与约束条件  
- 提前规划故障场景应对方案  
- 定义清晰的成功指标  

### 执行  
- 从最小可行团队开始（{{agent_count}} 个代理）  
- 根据实际观察到的需求扩展规模  
- 保持清晰的沟通渠道  
- 按里程碑跟踪进度  

### 学习  
- 收集性能数据用于分析  
- 识别成功与失败中的模式  
- 优化代理选择与协调策略  
- 在未来的编排中共享经验教训

---
name: agent-organization-expert
description: Multi-agent orchestration skill for team assembly, task decomposition, workflow optimization, and coordination strategies to achieve optimal team performance and resource utilization.
---

# Agent Organization

Assemble and coordinate multi-agent teams through systematic task analysis, capability mapping, and workflow design.

## Configuration

- **Agent Count**: {{agent_count}}
- **Task Type**: {{task_type}}
- **Orchestration Pattern**: {{orchestration_pattern}}
- **Max Concurrency**: {{max_concurrency}}
- **Timeout (seconds)**: ${timeout_seconds:300}
- **Retry Count**: ${retry_count:3}

## Core Process

1. **Analyze Requirements**: Understand task scope, constraints, and success criteria
2. **Map Capabilities**: Match available agents to required skills
3. **Design Workflow**: Create execution plan with dependencies and checkpoints
4. **Orchestrate Execution**: Coordinate {{agent_count}} agents and monitor progress
5. **Optimize Continuously**: Adapt based on performance feedback

## Task Decomposition

### Requirement Analysis
- Break complex tasks into discrete subtasks
- Identify input/output requirements for each subtask
- Estimate complexity and resource needs per component
- Define clear success criteria for each unit

### Dependency Mapping
- Document task execution order constraints
- Identify data dependencies between subtasks
- Map resource sharing requirements
- Detect potential bottlenecks and conflicts

### Timeline Planning
- Sequence tasks respecting dependencies
- Identify parallelization opportunities (up to {{max_concurrency}} concurrent)
- Allocate buffer time for high-risk components
- Define checkpoints for progress validation

## Agent Selection

### Capability Matching
Select agents based on:
- Required skills versus agent specializations
- Historical performance on similar tasks
- Current availability and workload capacity
- Cost efficiency for the task complexity

### Selection Criteria Priority
1. **Capability fit**: Agent must possess required skills
2. **Track record**: Prefer agents with proven success
3. **Availability**: Sufficient capacity for timely completion
4. **Cost**: Optimize resource utilization within constraints

### Backup Planning
- Identify alternate agents for critical roles
- Define failover triggers and handoff procedures
- Maintain redundancy for single-point-of-failure tasks

## Team Assembly

### Composition Principles
- Ensure complete skill coverage for all subtasks
- Balance workload across {{agent_count}} team members
- Minimize communication overhead
- Include redundancy for critical functions

### Role Assignment
- Match agents to subtasks based on strength
- Define clear ownership and accountability
- Establish communication channels between dependent roles
- Document escalation paths for blockers

### Team Sizing
- Smaller teams for tightly coupled tasks
- Larger teams for parallelizable workloads
- Consider coordination overhead in sizing decisions
- Scale dynamically based on progress

## Orchestration Patterns

### Sequential Execution
Use when tasks have strict ordering requirements:
- Task B requires output from Task A
- State must be consistent between steps
- Error handling requires ordered rollback

### Parallel Processing
Use when tasks are independent ({{orchestration_pattern}}):
- No data dependencies between tasks
- Separate resource requirements
- Results can be aggregated after completion
- Maximum {{max_concurrency}} concurrent operations

### Pipeline Pattern
Use for streaming or continuous processing:
- Each stage processes and forwards results
- Enables concurrent execution of different stages
- Reduces overall latency for multi-step workflows

### Hierarchical Delegation
Use for complex tasks requiring sub-orchestration:
- Lead agent coordinates sub-teams
- Each sub-team handles a domain
- Results aggregate upward through hierarchy

### Map-Reduce
Use for large-scale data processing:
- Map phase distributes work across agents
- Each agent processes a partition
- Reduce phase combines results

## Workflow Design

### Process Structure
1. **Entry point**: Validate inputs and initialize state
2. **Execution phases**: Ordered task groupings
3. **Checkpoints**: State persistence and validation points
4. **Exit point**: Result aggregation and cleanup

### Control Flow
- Define branching conditions for alternative paths
- Specify retry policies for transient failures (max ${retry_count:3} retries)
- Establish timeout thresholds per phase (${timeout_seconds:300}s default)
- Plan graceful degradation for partial failures

### Data Flow
- Document data transformations between stages
- Specify data formats and validation rules
- Plan for data persistence at checkpoints
- Handle data cleanup after completion

## Coordination Strategies

### Communication Patterns
- **Direct**: Agent-to-agent for tight coupling
- **Broadcast**: One-to-many for status updates
- **Queue-based**: Asynchronous for decoupled tasks
- **Event-driven**: Reactive to state changes

### Synchronization
- Define sync points for dependent tasks
- Implement waiting mechanisms with timeouts (${timeout_seconds:300}s)
- Handle out-of-order completion gracefully
- Maintain consistent state across agents

### Conflict Resolution
- Establish priority rules for resource contention
- Define arbitration mechanisms for conflicts
- Document rollback procedures for deadlocks
- Prevent conflicts through careful scheduling

## Performance Optimization

### Load Balancing
- Distribute work based on agent capacity
- Monitor utilization and rebalance dynamically
- Avoid overloading high-performing agents
- Consider agent locality for data-intensive tasks

### Bottleneck Management
- Identify slow stages through monitoring
- Add capacity to constrained resources
- Restructure workflows to reduce dependencies
- Cache intermediate results where beneficial

### Resource Efficiency
- Pool shared resources across agents
- Release resources promptly after use
- Batch similar operations to reduce overhead
- Monitor and alert on resource waste

## Monitoring and Adaptation

### Progress Tracking
- Monitor completion status per task
- Track time spent versus estimates
- Identify tasks at risk of delay
- Report aggregated progress to stakeholders

### Performance Metrics
- Task completion rate and latency
- Agent utilization and throughput
- Error rates and recovery times
- Resource consumption and cost

### Dynamic Adjustment
- Reallocate agents based on progress
- Adjust priorities based on blockers
- Scale team size based on workload
- Modify workflow based on learning

## Error Handling

### Failure Detection
- Monitor for task failures and timeouts (${timeout_seconds:300}s threshold)
- Detect agent unavailability promptly
- Identify cascade failure patterns
- Alert on anomalous behavior

### Recovery Procedures
- Retry transient failures with backoff (up to ${retry_count:3} attempts)
- Failover to backup agents when needed
- Rollback to last checkpoint on critical failure
- Escalate unrecoverable issues

### Prevention
- Validate inputs before execution
- Test agent availability before assignment
- Design for graceful degradation
- Build redundancy into critical paths

## Quality Assurance

### Validation Gates
- Verify outputs at each checkpoint
- Cross-check results from parallel tasks
- Validate final aggregated results
- Confirm success criteria are met

### Performance Standards
- Agent selection accuracy target: >${agent_selection_accuracy:95}%
- Task completion rate target: >${task_completion_rate:99}%
- Response time target: <${response_time_threshold:5} seconds
- Resource utilization: optimal range ${utilization_min:60}-${utilization_max:80}%

## Best Practices

### Planning
- Invest time in thorough task analysis
- Document assumptions and constraints
- Plan for failure scenarios upfront
- Define clear success metrics

### Execution
- Start with minimal viable team ({{agent_count}} agents)
- Scale based on observed needs
- Maintain clear communication channels
- Track progress against milestones

### Learning
- Capture performance data for analysis
- Identify patterns in successes and failures
- Refine selection and coordination strategies
- Share learnings across future orchestrations