arena

Spawns parallel candidate solutions, cross-judges them, and synthesizes the strongest result.

Updated Sep 2, 2026
One-click install
npx skills add https://github.com/jnyross/pstack-muse --skill arena-jnyross
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: arena
Source: https://github.com/jnyross/pstack-muse/tree/main/skills/arena
Command: npx skills add https://github.com/jnyross/pstack-muse --skill arena-jnyross

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? A single attempt at a non-trivial artifact often locks in the wrong design shape early. This Skill runs multiple independent attempts at the same task in parallel, evaluates them against a concrete rubric, and merges the best ideas into one verified result. ## Core Features & Use Cases - Parallel Fan-Out: Spawns N subagent candidates on the same prompt, each writing to its own isolated output path with a mandatory design rationale. - Cross-Judging and Selection: Scores every candidate criterion-by-criterion against a rubric, cross-checked by an independent judge model, then picks the most maintainable 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 non-trivial module or document where one attempt would likely pick the wrong structure, run the arena to compare several design directions and ship the synthesized consensus. ## Quick Start Ask the AI to arena this task by spawning several parallel candidates and synthesizing 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 AI attempts at the same task in parallel?

Use the arena workflow: frame a shared prompt with a concrete rubric, then spawn N subagents in one message with run_in_background enabled. Each candidate writes to its own output path and produces both the artifact and a rationale.

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

Score each candidate criterion by criterion against a rubric derived from the task, and compare your scores with an independent cross-judge subagent. Pick the base a future maintainer can extend most easily, then graft the best ideas from the losers.

When should I use parallel candidate generation instead of a single attempt?

Use it for non-trivial artifacts where one attempt would lock in the wrong shape, such as ambiguous design or architecture tasks. Skip it for simple, well-specified work where the overhead of N candidates adds no signal.

What happens if one parallel candidate fails to produce output?

The workflow proceeds with the remaining N-1 candidates and records the dropout in the synthesis note. The final record names the base, grafts, rejections, dropouts, and the verification result.

Why do parallel candidates need separate output paths?

Each candidate writes to its own git worktree or a unique /tmp directory so no shared mutable state exists between them. N candidates writing to the same path would corrupt outputs and violate the separate-before-serializing-shared-state principle.