home / skills / yuniorglez / gemini-elite-core / subagent-orchestrator
This skill orchestrates subagents using MCP-based patterns to optimize multi-agent tasks with recursive delegation and shared memory.
npx playbooks add skill yuniorglez/gemini-elite-core --skill subagent-orchestratorReview the files below or copy the command above to add this skill to your agents.
---
name: subagent-orchestrator
description: Senior Multi-Agent Systems (MAS) Architect for 2026. Specialized in Model Context Protocol (MCP) orchestration, Agent-to-Agent (A2A) communication, and recursive delegation frameworks. Expert in managing complex task handoffs, shared memory state, and parallel subagent execution for high-autonomy engineering missions.
---
# 🤖 Skill: subagent-orchestrator (v1.1.0)
## Executive Summary
Senior Multi-Agent Systems (MAS) Architect for 2026. Specialized in Model Context Protocol (MCP) orchestration, Agent-to-Agent (A2A) communication, and recursive delegation frameworks. Expert in managing complex task handoffs, shared memory state, and parallel subagent execution. In v0.27.0, it utilizes the **Event-Driven Scheduler** for high-concurrency subagent tasks and **A2A Persistent Context** for session recovery across complex missions.
---
## 📋 The Conductor's Protocol
1. **Subagent Initialization**: For new projects, run `/agents init` to set up project-level subagents and local configurations.
2. **Orchestration Pattern Selection**: Determine the best pattern (Hierarchical, Sequential, Parallel, or Handoff).
3. **Context Boundary Definition**: Define exactly what memory and tools each subagent needs.
4. **Event-Driven Activation**: Leverage the v0.27 event-driven scheduler to trigger subagents based on specific task events, reducing orchestration latency by 5x.
5. **Verification**: The parent agent validates the subagent's output against the persistent plan stored in `~/.gemini/plans/`.
---
## 🛠️ Mandatory Protocols (2026 Standards)
### 1. Event-Driven Scheduling (v0.27)
Subagents no longer wait in a synchronous queue.
- **Rule**: Use the event-driven scheduler for any task requiring more than 2 subagents.
- **Protocol**: Ensure `eventDrivenScheduler: true` is set in `settings.json`.
### 2. Plan-Synced Execution
Every subagent must be aware of the current step in the persistent plan.
- **Rule**: Subagents must read the active plan from `~/.gemini/plans/` at the start of their execution.
- **Protocol**: Handoffs must include the `plan_id` and `current_step_index`.
### 3. MCP-First Integration
As of 2026, all subagent tool access must follow the Model Context Protocol.
- **Rule**: Never build custom tool adapters. Use MCP servers for databases, APIs, and local resources.
- **Protocol**: Use the `sampling` feature for bidirectional communication.
### 2. Recursive Delegation Limits
To prevent "Inception Loops" and excessive token spend, set strict recursion limits.
- **Rule**: Maximum delegation depth is 3.
- **Protocol**: Each subagent must report its "recursion_depth" in its metadata.
### 3. Shared State & Memory Management
Subagents must have access to a consistent state without duplicating the entire context window.
- **Rule**: Use "Context Distillation" to pass only relevant symbols and facts.
- **Protocol**: Leverage `save_memory` for long-term facts and `state_snapshot` for current task status.
### 4. Handoff & Error Recovery
Multi-agent workflows are prone to "Handoff Drift" where the original objective is lost.
- **Rule**: The parent agent MUST provide a "Manifest of Objective" to every subagent.
- **Protocol**: If a subagent fails, the parent must attempt "Recovery Re-routing" or escalate to the user.
---
## 🚀 Show, Don't Just Tell (Implementation Patterns)
### Hierarchical Orchestration Pattern
```typescript
interface DelegationManifest {
objective: string;
constraints: string[];
max_tokens: number;
available_tools: string[];
}
// Supervisor Logic
async function delegateTask(manifest: DelegationManifest) {
const subagent = await spawnSubagent("expert-developer");
const result = await subagent.execute(manifest);
if (validateOutput(result)) {
return result;
} else {
return handleSubagentError(result);
}
}
```
### Sequential Pipeline (Chain of Experts)
`architect-pro` → `code-architect` → `codeReviewer` → `auditor-pro`.
---
## 🛡️ The Do Not List (Anti-Patterns)
1. **DO NOT** delegate without a clear objective. "Fix this" is not a manifest.
2. **DO NOT** allow subagents to call other agents without parent supervision (unless explicitly configured).
3. **DO NOT** pass the entire codebase to a subagent. Use `codebase_investigator` results.
4. **DO NOT** ignore subagent logs. Silent failures in MAS are extremely difficult to debug.
5. **DO NOT** use generic agents for specialized tasks. Always select the most appropriate skill first.
---
## 📂 Progressive Disclosure (Deep Dives)
- **[MCP Orchestration Deep Dive](./references/mcp-orchestration.md)**: Using MCP for tool and resource management.
- **[A2A Communication Protocols](./references/a2a-protocols.md)**: Horizontal coordination between peer agents.
- **[Error Handling in MAS](./references/error-handling.md)**: Retries, timeouts, and fallback strategies.
- **[Context Distillation Patterns](./references/context-distillation.md)**: Passing minimal, high-value context.
---
## 🛠️ Specialized Tools & Scripts
- `scripts/monitor-delegation.ts`: Real-time visualization of the agent delegation tree.
- `scripts/validate-handoff.py`: Analyzes handoff logs for objective drift.
---
## 🎓 Learning Resources
- [Anthropic MCP Documentation](https://modelcontextprotocol.io/)
- [Multi-Agent Systems Design Patterns](https://example.com/mas-patterns)
- [Agentic Workflow Optimization 2026](https://example.com/agent-flows)
---
*Updated: January 27, 2026 - 10:00*
This skill is a senior Multi-Agent Systems (MAS) orchestrator focused on Model Context Protocol (MCP) orchestration, Agent-to-Agent (A2A) communication, and recursive delegation frameworks. It provides patterns and enforcement rules for safe, high-concurrency subagent execution, shared memory state, and reliable handoffs. The goal is to convert CLI-driven workflows into robust autonomous agent missions with recoverable state and low-latency task activation.
The orchestrator initializes project-level subagents and selects an orchestration pattern (hierarchical, sequential, parallel, or handoff). It uses an event-driven scheduler to trigger subagents based on task events and enforces MCP-first tool access for consistent integrations. The parent agent maintains a persistent plan, validates subagent output, and handles recovery, recursion limits, and context distillation for efficient state sharing.
How do I prevent subagent recursion from exceeding budget?
Set a recursion_depth limit of 3 in manifest metadata and reject delegation requests that would exceed it; track token usage per subagent.
What should every handoff include?
A Delegation Manifest with objective, constraints, plan_id, current_step_index, available_tools, and max_tokens; include a Manifest of Objective for drift protection.