eval
Rank finished AgentHub agents by metric or judge. Use when asked which agent won.
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Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- /agenthub:eval — Evaluate Agent Results
- Usage
- What It Does
- Metric Mode (eval command configured)
- LLM Judge Mode (no eval command, or --judge flag)
- Hybrid Mode
- After Eval
Rank all agent results for a session. Supports metric-based evaluation (run a command), LLM judge (compare diffs), or hybrid. Run the evaluation command in each agent's worktree: Output: For each agent: 1. Get the diff: git diff {basebranch}...{agentbranch} 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: 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 1. Update session state: 2. Tell the user: Ranked results with winner highlighted Next step: /agenthub:merge to merge the winner Or /agenthub:merge {session-id} --agent {winner} to be explicit
- Source repository
- alirezarezvani/claude-skills
- Source file
- engineering/agenthub/skills/eval/SKILL.md
- Fixed revision
- 8f082544116fc0d014188e521ca40304cdfc86b0
Sources and evidence
This record was collected automatically. Its documentation, suitability and runtime behavior have not been manually verified.
Author: alirezarezvani/claude-skills · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T14:01:46.554Z.
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