Agent Handoff Orchestrator is a skill that addresses context collapse in multi-agent workflows and long-running AI sessions. When tasks are delegated between agents, standard LLM prompts often accumulate drift — the receiving agent loses track of what has been verified, what has been assumed, and what the original goals were. This skill generates high-density, structured handoff packets in Markdown format designed specifically for LLM comprehension.

Each packet enforces a clear engineering standard for state transfer. It separates verified facts from assumptions, defines authority boundaries for the receiving agent, and establishes explicit return contracts so the handoff can flow back to the originating context. The output includes a mission statement, scope constraints, and observable success criteria, giving the receiver — whether a coder, reviewer, QA agent, or human operator — enough structured context to begin productive work without rediscovering prior decisions.

This skill is intended for orchestration layers in multi-agent pipelines where session persistence and clean delegation are requirements. It does not execute code, query databases, or call external APIs. Its sole output is a structured handoff document. It suits pipelines that route work across specialized agents and need a durable, unambiguous record of state at each transition point.

Use cases

Generate a handoff packet when delegating a coding task from a planner agent to a coder agent

Preserve session state across long-running workflows that span multiple LLM context windows

Create a structured transition document when handing off work from an AI agent to a human operator

Define explicit return contracts so a downstream QA agent knows what deliverables to expect

Capture verified facts versus assumptions at a workflow checkpoint to prevent downstream errors

Produce scope constraints that prevent a receiving agent from exceeding its authority

When to use it

Building multi-agent pipelines where specialized agents hand tasks to one another

Managing long-running sessions where context must survive across separate LLM calls

Coordinating between AI agents and human operators who need a clear state summary

Enforcing consistent delegation standards across an agent orchestration framework

Reducing ambiguity at role boundaries such as planner-to-coder or coder-to-reviewer transitions

When not to use it

When the workflow is single-agent and has no handoff or delegation step

When the task requires executing code, querying a database, or calling external APIs

When the receiving system cannot consume structured Markdown documents

When a lightweight free-text summary is sufficient and formal state transfer is not needed