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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill eval- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads eval automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
/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
FAQ
What does the eval skill do?
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 eval?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill eval in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/eval/ (personal) or .claude/skills/eval/ (project).
Which AI agents can use eval?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is eval safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains eval?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 1,409 recorded installs.
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