brovzar-lab/carpetify/eval

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Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Install

npx vskill@latest install brovzar-lab/carpetify/eval

SKILL.md

/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
  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

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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