What problem does it solve? Coordinating multiple specialized AI agents — parallel researchers, persistent coders, independent reviewers, human approvers — inside a single agent loop loses context isolation, auditability, and failure recovery. This Skill lets you author, validate, run, and resume pi-graph JSON definitions that orchestrate multi-agent workflows with explicit state, typed handoffs, and durable checkpoints. ## Core Features & Use Cases - Graph authoring with three context modes: Choose isolated, thread, or shared context per agent node to control memory, independence, and auditability across loops. - Schema-validated handoffs and routing: Enforce JSON schemas on agent outputs, define conditional edges, fan-out/barrier patterns, and reducers for parallel write conflicts. - Durable runs with human gates: Pause at human approval nodes, resume from checkpoints, inspect run state, and recover from failures with retry and routing strategies. - Use Case: Design a research-review workflow where parallel researchers fan out, a thread-mode writer drafts iteratively, and an isolated reviewer rejects and routes work back until approved. ## Quick Start Ask the AI to design and validate a pi-graph JSON for a research-and-review workflow with a human approval gate, then run it with a task description.