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:
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:
python {skill_path}/scripts/session_manager.py --update {session-id} --state evaluating2. 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