Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.
cd ~/.claude/skills
git clone https://github.com/alirezarezvani/claude-skills.git claude-skills mkdir -p ~/.claude/skills/eval
curl -fsSL https://raw.githubusercontent.com/alirezarezvani/claude-skills/HEAD/.gemini/skills/eval/SKILL.md \
-o ~/.claude/skills/eval/SKILL.md Rank all agent results for a session. Supports metric-based evaluation (run a command), LLM judge (compare diffs), or hybrid.
/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)
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)
For each agent:
git diff {base_branch}...{agent_branch}.agenthub/board/results/agent-{i}-result.mdPresent 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)
python {skill_path}/scripts/session_manager.py --update {session-id} --state evaluating
/hub:merge to merge the winner/hub:merge {session-id} --agent {winner} to be explicitUse when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use when executing implementation plans with independent tasks in the current session
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code
Use when creating new skills, editing existing skills, or verifying skills work before deployment