Eval

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

作者 alirezarezvani19392f7a0826無授權條款27K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫5 週前更新

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.

僅含說明AI & Agents
AI 產生的概覽

依指標、LLM 評審或混合方式,為已完成的 AgentHub 代理結果排名並選出優勝者。

功能
此技能會評估並排名某個 AgentHub 工作階段中的所有代理結果。它支援指標模式,也就是在每個代理的工作區執行已設定的評估指令,並依指定指標與方向排名;也支援 LLM 評審模式,依正確性、簡潔性與品質比較各代理的差異與結果貼文。混合模式會先執行指標評估,再於差距 10% 以內時用評審打破平手。它會輸出附有醒目優勝者的排名表,並更新工作階段狀態。
適用情境
當使用者執行 /hub:eval,或要求為已完成的 AgentHub 代理評分、比較或選出優勝者時使用。適用於存在多個代理結果、需要在合併前選出最佳結果的工作階段。
執行需求
需要一個包含已完成代理結果,以及可檢查的 git 分支或工作區的 AgentHub 工作階段。指標模式需要已設定的評估指令與指標;LLM 評審模式需要 LLM 評審器。文件引用了 result_ranker.py 與 session_manager.py 指令碼,但此技能並未隨附任何指令碼。

/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

來源與署名

來源:alirezarezvani/claude-skills位於engineering/agenthub/skills/eval提交19392f7

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