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agent-coordination

Coordinate multiple agents to decompose tasks and share results via memory files.

Updated Mar 18, 2026
Search Tags:#subagents#agent#multi-agent#orchestration#memory#coordination#handoffs
One-click install
npx skills add https://github.com/ignidus/iscagent --skill agent-coordination-ignidus
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-coordination
Source: https://github.com/ignidus/iscagent/tree/main/skills/agent-coordination
Command: npx skills add https://github.com/ignidus/iscagent --skill agent-coordination-ignidus

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Memory-based coordination enables multiple AI agents to work together by reading inputs from and writing outputs to a shared, centralized memory model. This approach avoids direct messaging bottlenecks and provides a clear, auditable record of decisions and results.

Core Features & Use Cases

  • Memory-layout protocol: coordinator.md captures task decomposition, and per-role handoff files record results for easy integration.
  • Parallel subagent execution: spawn up to 6-8 agents that work in parallel with clearly defined handoffs and status signals.
  • Guardrails and rules: enforce task scope, status reporting, and safe cleanup to prevent drift or leakage of context.
  • Use cases: coordinating data collection, synthesis, and decision-making across specialized subagents in complex projects.

Quick Start

Spawn a coordinator and up to six subagents, then write and integrate handoff files to memory/runtime using the Agent tool.

Frequently Asked Questions about agent-coordination

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate multiple AI agents to work together on a complex task?▼

Multi-agent coordination uses a shared memory model where a coordinator agent decomposes tasks and writes them to memory files, while subagents read inputs and write outputs back to the same shared memory, avoiding direct messaging bottlenecks.

What's the best way to run parallel subagents without context drift?▼

Parallel subagent execution is managed through a strict handoff protocol with clear status signals, using guardrails that enforce task scope, status reporting, and safe cleanup to prevent context drift or leakage across up to 8 concurrent agents.

How does memory-based coordination work for AI agent handoffs?▼

Memory-based coordination works by writing task decomposition to a coordinator.md file and recording individual agent results in per-role handoff files, creating an auditable record of decisions that avoids direct messaging bottlenecks.

Do I need external dependencies to orchestrate multi-agent workflows with memory?▼

No external dependencies are required to orchestrate multi-agent workflows with memory; the system relies solely on standard file I/O operations for reading inputs and writing outputs to runtime files.

Can I spawn more than 8 parallel subagents for task decomposition?▼

The system is designed to spawn up to 6-8 agents working in parallel with clearly defined handoffs; exceeding this range is not specified and may impact the effectiveness of the guardrails and coordination protocol.

When should I use a shared memory model instead of direct messaging for agent orchestration?▼

A shared memory model is ideal for complex projects requiring data collection, synthesis, and decision-making across specialized subagents, providing a clear auditable record of decisions while avoiding direct messaging bottlenecks.

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