Skills Artificial Intelligence Evaluate and Rank Agent Results

Evaluate and Rank Agent Results

v20260612
eval
This tool allows users to systematically evaluate and rank the outputs of multiple agents within an AgentHub session. It supports metric-based evaluation (running custom commands for measurable metrics like speed or resource usage), or an advanced LLM Judge Mode. The LLM Judge compares results based on criteria like correctness, simplicity, and overall quality, helping users determine the best-performing agent for the task.
Get Skill
155 downloads
Overview

/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:

  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:
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
Info
Name eval
Version v20260612
Size 2.42KB
Updated At 2026-06-13
Language