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: ```bash 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: 1. Get the diff: `git diff {base_branch}...{agent_branch}` 2. Read the agent's result post from `.agenthub/board/results/agent-{i}-result.md` 3. 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 1. Run metric evaluation first 2. If top agents are within 10% of each other, use LLM judge to break ties 3. Present both metric and qualitative rankings ## After Eval 1. Update session state: ```bash python {skill_path}/scripts/session_manager.py --update {session-id} --state evaluating ``` 2. 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