arena

Spawns parallel candidate solutions, judges them against a rubric, and synthesizes the strongest result.

Updated Jul 29, 2026
One-click install
npx skills add https://github.com/mmdmcy/fluttAIrbar --skill arena-mmdmcy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arena
Source: https://github.com/mmdmcy/fluttAIrbar/tree/main/plugins/pstack/skills/arena
Command: npx skills add https://github.com/mmdmcy/fluttAIrbar --skill arena-mmdmcy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? A single attempt at a non-trivial artifact often locks in the wrong design early. This Skill runs multiple parallel attempts at the same task, evaluates them against an explicit rubric, and merges the best ideas into one verified result instead of betting on one shot. ## Core Features & Use Cases - Parallel fan-out: Spawns N background subagents with the same framed prompt, each writing to its own isolated output path with a mandatory rationale. - Cross-judging and rubric scoring: A readonly judge subagent on a different model family scores every candidate criterion by criterion, and the parent reads all candidates end to end before picking a base. - Grafting and verification: Ports the strongest ideas from losing candidates into the base by hand, records rejections, and verifies the synthesized artifact. - Use Case: When designing a new CLI interface or module architecture, run $arena to generate four candidate designs, have a judge score them, then ship a synthesized design that combines the cleanest base with the best ideas from the rest. ## Quick Start Use $arena to generate four parallel candidate implementations of this task, judge them against a rubric, and synthesize the strongest result.

Frequently Asked Questions about arena

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multiple parallel AI attempts at the same coding task?▼

Use the arena workflow: frame one shared prompt with a rubric, then spawn N background subagents in a single message, each writing to its own output path. After all finish, read every candidate, pick a base, and graft the best ideas from the losers.

How to choose the best result from multiple AI-generated candidates?▼

Score each candidate criterion by criterion against a rubric of 3-6 concrete gradeable items rather than judging holistically. A readonly cross-judge subagent on a different model family provides an independent verdict to compare against your own reading.

When should I use the arena workflow instead of a single attempt?▼

Use it for non-trivial artifacts where one attempt would lock in the wrong shape, such as new designs or architecture decisions. Skip it for simple or well-understood tasks where parallel generation adds cost without reducing design risk.

What happens if one arena candidate fails to produce output?▼

The workflow proceeds with N-1 candidates and records the dropout in the synthesis note. The remaining candidates are still judged, grafted, and verified normally.

Why do arena candidates need isolated output paths?▼

Each candidate writes to its own git worktree or a separate /tmp/arena directory. Sharing one path across N writers creates shared mutable state, which corrupts outputs and makes judging unreliable.