agentclash-hub

AI agent evaluation tasks are coordinated through a structured workflow map that manages skill dependencies across CLI setup, challenge pack authoring, eval execution, and scorecard analysis. This prevents fragmented efforts during end-to-end lifecycle orchestration.

The canonical sequence for running an AI agent evaluation involves configuring the runtime, authoring a challenge pack, and executing the eval runner. This establishes the correct dependency order from initial CLI setup through to final scorecard analysis.

Yes, the AgentClash CLI is required to manage agent evaluation tasks. Access to the CLI and the official API endpoint is necessary to maintain state and facilitate workspace operations across the evaluation ecosystem.

Agent benchmarking can be integrated into CI/CD pipelines through release gating. The workflow map includes CI/CD operations as part of the end-to-end lifecycle, allowing automated eval execution and scorecard analysis to gate releases.

Scorecard analysis UI is accessible through the product navigation directory provided by the hub skill. After eval execution, this directory links to the appropriate web UI surfaces for run management and scorecard analysis.