eval
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.
How do I install this agent skill?
npx skills add https://github.com/alirezarezvani/claude-skills --skill evalIs this agent skill safe to install?
- Gen Agent Trust Hubwarn
The skill facilitates the evaluation of agent results through custom command execution and LLM-based judging. It poses a security risk because it executes arbitrary commands defined in the evaluation configuration and processes untrusted agent outputs that could contain instructions designed to manipulate the evaluation results.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
- ZeroLeakspass
Score: 93/100 · 2 sections analyzed
What does this agent skill do?
/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:mergeto merge the winner - Or
/hub:merge {session-id} --agent {winner}to be explicit
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/alirezarezvani/claude-skills/eval">View eval on skillZs</a>