eval

/hub:eval — Evaluate Agent Results Rank all agent results for a session. Supports metric-based evaluation (run a command), LLM judge (compare diffs), or hybrid. Usage /hub:eval # Eval latest session using configured criteria /hub:eval 20260317-143022 # Eval specific session /hub:eval --judge # Force LLM judge mode (ignore metric config) What It Does Metric Mode (eval command configured) Run the evaluation command in each agent's worktree: python { skill_path } /scripts/result_ranker.py \ --session { session-id } \ --eval-cmd "{eval_cmd}" \ --metric { metric } --direction { direction } Output: RANK AGENT METRIC DELTA FILES 1 agent-2 142ms -38ms 2 2 agent-1 165ms -15ms 3 3 agent-3 190ms +10ms 1 Winner: agent-2 (142ms) LLM Judge Mode (no eval command, or --judge flag) For each agent: Get the diff: git diff {base_branch}...{agent_branch} Read the agent's result post from .agenthub/board/results/agent-{i}-result.md Compare all diffs and rank by: Correctness — Does it solve the task? Simplicity — Fewer lines changed is better (when equal correctness) Quality — Clean execution, good structure, no regressions Present rankings with justification. Example LLM judge output for a content task: RANK AGENT VERDICT WORD COUNT 1 agent-1 Strong narrative, clear CTA 1480 2 agent-3 Good data points, weak intro 1520 3 agent-2 Generic tone, no differentiation 1350 Winner: agent-1 (strongest narrative arc and call-to-action) Hybrid Mode Run metric evaluation first If top agents are within 10% of each other, use LLM judge to break ties Present both metric and qualitative rankings After Eval Update session state: python { skill_path } /scripts/session_manager.py --update { session-id } --state evaluating Tell the user: Ranked results with winner highlighted Next step: /hub:merge to merge the winner Or /hub:merge {session-id} --agent {winner} to be explicit