coordinate-handoffs

Validate sub-agent artifacts and sequence phase transitions in multi-agent workflows.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/bketelsen/truenas-mcp --skill coordinate-handoffs
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: coordinate-handoffs
Source: https://github.com/bketelsen/truenas-mcp/tree/main/.gaai/core/skills/delivery/coordinate-handoffs
Command: npx skills add https://github.com/bketelsen/truenas-mcp --skill coordinate-handoffs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures that AI agent workflows proceed smoothly by validating the output of each sub-agent, managing retries, and determining the correct next step, preventing workflow stagnation or incorrect execution.

Core Features & Use Cases

  • Artifact Validation: Verifies the structure and content of output files from planning, implementation, and QA agents.
  • Workflow Sequencing: Determines whether to proceed, retry, or escalate based on validation results and retry limits.
  • Retry & Escalation Logic: Manages automatic retries for failed sub-agent tasks and escalates complex or persistent issues to human oversight.
  • Use Case: After an implementation agent finishes coding a feature, this skill checks the generated code report. If the report is valid, it allows the workflow to proceed to the QA phase; if not, it may trigger a retry or escalate the issue.

Quick Start

Use the coordinate-handoffs skill to validate the output from the planning agent and decide the next action.

Frequently Asked Questions about coordinate-handoffs

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

FAQPage Schema
How do I validate sub-agent handoff artifacts in multi-agent AI workflows?

To validate sub-agent handoff artifacts, you verify the structure and content of output files from planning, implementation, and QA agents against defined artifact schemas. This ensures deterministic workflow progression by confirming successful phase completion.

How do I manage retry logic for failed AI agent tasks during workflow transitions?

Managing retry logic for failed AI agent tasks involves checking validation results against configured retry limits. If an artifact fails validation, the workflow automatically triggers a retry for the sub-agent task before escalating persistent issues.

What is the best way to orchestrate phase transitions between planning, implementation, and QA agents?

Orchestrating phase transitions requires sequencing workflow steps based on artifact validation. If a generated report is valid, the workflow proceeds to the next phase; if not, it either retries the task or escalates the issue.

When should I escalate persistent sub-agent failures instead of retrying the workflow?

You should escalate persistent sub-agent failures when automatic retries exceed defined retry limits. Escalation manages complex issues by routing persistent workflow stagnation or incorrect execution to human oversight.

Does coordinate-handoffs work without predefined artifact schemas for AI agent workflows?

No, coordinate-handoffs requires adherence to defined artifact schemas and retry limits for deterministic workflow progression. Schema validation is the core mechanism for verifying output structure and managing phase transitions.

Why does my multi-agent workflow stagnate after the implementation phase finishes coding?

Multi-agent workflow stagnation occurs when implementation output fails artifact validation, preventing phase progression. Sequencing logic determines whether to retry the sub-agent, escalate the issue, or proceed to QA.