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Skills/alirezarezvani/claude-skills/eval
eval logo

eval

alirezarezvani/claude-skills
1K installs18K stars
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Installation

npx skills add https://github.com/alirezarezvani/claude-skills --skill eval

Summary

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

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:

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

Score

0–100
65/ 100

Grade

C

Popularity17/30

1,368 installs — growing adoption. Source repo has 18,066 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Eval skill score badge previewScore badge

Markdown

[![Eval skill](https://www.remoteopenclaw.com/skills/alirezarezvani/claude-skills/eval/badges/score.svg)](https://www.remoteopenclaw.com/skills/alirezarezvani/claude-skills/eval)

HTML

<a href="https://www.remoteopenclaw.com/skills/alirezarezvani/claude-skills/eval"><img src="https://www.remoteopenclaw.com/skills/alirezarezvani/claude-skills/eval/badges/score.svg" alt="Eval skill"/></a>

Eval FAQ

How do I install the Eval skill?

Run “npx skills add https://github.com/alirezarezvani/claude-skills --skill eval” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Eval skill do?

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Eval skill free?

Yes. Eval is a free, open-source skill published from alirezarezvani/claude-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Eval work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Eval works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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