Eval

alirezarezvani/claude-skills/engineering/agenthub/skills/eval

作者 alirezarezvani19392f7a0826无许可证27K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库5周前更新

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.

仅含说明AI & Agents
AI 生成的概览

按指标、LLM 评审或混合方式对已完成的 AgentHub 智能体结果进行排名并选出优胜者。

功能
该技能评估并排名某个 AgentHub 会话中的所有智能体结果。它支持指标模式,即在每个智能体工作区运行配置好的评估命令,并按指定指标和方向排名;也支持 LLM 评审模式,根据正确性、简洁性和质量比较各智能体的差异和结果帖。混合模式先运行指标评估,再在差距 10% 以内时用评审打破平局。它会输出带高亮优胜者的排名表并更新会话状态。
适用场景
当用户运行 /hub:eval,或要求对已完成的 AgentHub 智能体进行评分、比较或选出优胜者时使用。适用于存在多个智能体结果、需要在合并前选出最佳结果的会话。
运行要求
需要一个包含已完成智能体结果以及可检查的 git 分支或工作区的 AgentHub 会话。指标模式需要配置好的评估命令和指标;LLM 评审模式需要 LLM 评审器。文档引用了 result_ranker.py 和 session_manager.py 脚本,但本技能未附带任何脚本。

/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      FILES1     agent-2     142ms       -38ms      22     agent-1     165ms       -15ms      33     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 COUNT1     agent-1  Strong narrative, clear CTA            14802     agent-3  Good data points, weak intro           15203     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
  1. 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

来源与署名

来源:alirezarezvani/claude-skills位于engineering/agenthub/skills/eval提交19392f7

许可证: 无许可证

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