Agent skill · data analytics · alirezarezvani
eval
Rank finished AgentHub agents by metric or judge. Use when asked which agent won.
Why this skill is useful
Adds specific commands and evaluation criteria for ranking agent results that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 1k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
/agenthub: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 What It Does Metric Mode (eval command configured) Run the evaluation command in each agent's worktree: Output: LLM Judge Mode (no eval command, or --judge flag) 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: 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: 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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/eval--37ba27