agent-teams-error-handling

What problem does it solve?

This Skill addresses the critical challenge of ensuring AI teams remain operational and data integrity is maintained even when individual team members encounter failures. It establishes robust patterns for detecting, handling, and recovering from partial or complete team member outages.

Core Features & Use Cases

• Error Detection: Implements mechanisms for a team lead agent to detect when a teammate agent fails during task execution.
• Dependency Management: Differentiates between mandatory and optional dependencies, allowing teams to continue operation with partial results when possible.
• Partial Result Handling: Defines patterns for saving successful task outputs and enabling subsequent tasks to proceed using this partial data.
• Failure Marking: Establishes a clear method for marking tasks as failed or skipped within the team's task management system.
• Use Case: In a multi-agent system processing financial reports, if one agent fails to fetch live market data (a mandatory dependency for a subsequent analysis task), this skill ensures the system doesn't halt entirely. Instead, it might proceed with a cached dataset or a partial analysis if another agent successfully provided backup data (an optional dependency).

Quick Start

Execute the agent-teams-error-handling skill to simulate and test error handling scenarios within an AI team.