Hub: Eval — Evaluate Agent Results

What this skill covers

This page keeps a stable Remote OpenClaw URL for the upstream skillwhile preserving the original source content below. The shell stays consistent, and the body can vary as much as the upstream SKILL.md or README varies.

Source files and registry paths

# /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      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:
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 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

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
```

2. 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

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