ai-agent-orchestrator
Эксперт по оркестрации AI агентов. Используй для multi-agent systems, agent coordination, task delegation и agent workflows.
Best use case
ai-agent-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Эксперт по оркестрации AI агентов. Используй для multi-agent systems, agent coordination, task delegation и agent workflows.
Teams using ai-agent-orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-agent-orchestrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-agent-orchestrator Compares
| Feature / Agent | ai-agent-orchestrator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Эксперт по оркестрации AI агентов. Используй для multi-agent systems, agent coordination, task delegation и agent workflows.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI Agent Orchestrator Expert
Эксперт по проектированию и реализации многоагентных систем.
## Основные принципы
### Иерархия агентов
- **Оркестратор**: Главный координатор, делегирует задачи
- **Специализированные агенты**: Для конкретных доменов (исследования, анализ, код)
- **Утилитарные агенты**: Вспомогательные функции (валидация, форматирование)
- **Мониторинг**: Здоровье системы и обработка ошибок
## Паттерны архитектуры
### Hub and Spoke
```python
class OrchestratorAgent:
def __init__(self):
self.agents = {
'researcher': ResearchAgent(),
'analyzer': AnalysisAgent(),
'writer': WritingAgent(),
'validator': ValidationAgent()
}
async def orchestrate_task(self, task):
subtasks = self.decompose_task(task)
results = []
for subtask in subtasks:
agent_type = self.route_task(subtask)
result = await self.agents[agent_type].execute(subtask)
results.append(result)
return self.synthesize_results(results)
```
### Pipeline Pattern
```python
class AgentPipeline:
def __init__(self):
self.stages = [
DataIngestionAgent(),
ProcessingAgent(),
AnalysisAgent(),
OutputAgent()
]
async def execute_pipeline(self, input_data):
data = input_data
for stage in self.stages:
try:
data = await stage.process(data)
except Exception as e:
return await self.handle_pipeline_error(stage, e, data)
return data
```
## Маршрутизация задач
```python
class TaskRouter:
def __init__(self):
self.agent_capabilities = {
'code_analysis': ['python', 'javascript', 'sql'],
'research': ['web_search', 'document_analysis'],
'writing': ['technical', 'creative', 'business']
}
self.agent_load = {}
def route_task(self, task):
required_skills = self.extract_skills(task)
capable_agents = [
agent_id for agent_id, skills in self.agent_capabilities.items()
if self.has_required_skills(skills, required_skills)
]
return self.select_least_loaded_agent(capable_agents)
```
## Межагентная коммуникация
```python
@dataclass
class AgentMessage:
sender_id: str
receiver_id: str
message_type: MessageType
payload: Dict[str, Any]
correlation_id: str
timestamp: float
priority: int = 5
class MessageBus:
async def send_message(self, message: AgentMessage):
await self.validate_message(message)
await self.route_message(message)
await self.log_message(message)
```
## Обработка ошибок
### Circuit Breaker
```python
class AgentCircuitBreaker:
def __init__(self, failure_threshold=5, timeout=60):
self.failure_count = 0
self.failure_threshold = failure_threshold
self.state = 'CLOSED'
async def call_agent(self, agent, task):
if self.state == 'OPEN':
if time.time() - self.last_failure_time > self.timeout:
self.state = 'HALF_OPEN'
else:
raise CircuitBreakerOpenError()
try:
result = await agent.execute(task)
if self.state == 'HALF_OPEN':
self.reset()
return result
except Exception as e:
self.record_failure()
raise
```
### Graceful Degradation
```python
class ResilientOrchestrator:
def __init__(self):
self.agent_priorities = {
'primary': ['gpt-4', 'claude-3'],
'fallback': ['gpt-3.5', 'local-model'],
'emergency': ['rule-based-agent']
}
async def execute_with_fallback(self, task):
for tier in ['primary', 'fallback', 'emergency']:
for agent_id in self.agent_priorities[tier]:
try:
if await self.is_agent_healthy(agent_id):
return await self.execute_on_agent(agent_id, task)
except Exception:
continue
raise AllAgentsFailedError()
```
## Лучшие практики
- Реализуйте комплексное логирование с correlation ID
- Отслеживайте метрики производительности агентов
- Используйте распределенную трассировку для сложных workflows
- Валидируйте всю межагентную коммуникацию
- Проектируйте агентов как stateless когда возможно
- Используйте очереди сообщений для развязкиRelated Skills
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