arena

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

3|2|Updated Aug 28, 2026
One-click install
npx skills add https://github.com/adjohn/pstack --skill arena-adjohn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arena
Source: https://github.com/adjohn/pstack/tree/main/skills/arena
Command: npx skills add https://github.com/adjohn/pstack --skill arena-adjohn

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 removes that risk by running multiple independent attempts in parallel and synthesizing the best ideas into one verified result. ## Core Features & Use Cases - Parallel Fan-Out: Spawns N subagents (optionally across different Claude model tiers like fable, sonnet, haiku, and opus) to attempt the same task independently, each writing to its own output path. - Rubric-Based Judging: Defines 3-6 concrete gradeable criteria up front, then uses a cross-judge subagent plus a full parent read-through to score every candidate criterion by criterion. - Graft and Synthesize: Picks the strongest candidate as a base, folds in the best ideas from losing candidates by hand, and records grafts, rejections, and dropouts in a synthesis note. - Use Case: When designing a new CLI interface or module architecture, run three candidates in the arena, compare their structures against the rubric, and ship a synthesized design that combines the cleanest boundaries from each. ## Quick Start Ask the agent to arena this task by spawning 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 parallel attempts at the same coding task?▼

Use the arena workflow: frame a shared prompt with a concrete rubric, spawn N subagents in one message with run_in_background set to true, and assign each its own output path such as a git worktree or /tmp/arena-<slug>/candidate-<n>/.

How do I compare outputs from different Claude model tiers?▼

Spawn one candidate per tier (fable, sonnet, haiku, opus) with the same prompt, then run a readonly cross-judge subagent on a different tier that scores each candidate against the rubric and recommends a base with rationale.

When should I use parallel candidates instead of a single attempt?▼

Use parallel candidates when one attempt at a non-trivial artifact would lock in the wrong shape, such as architecture or interface design. Skip the arena for trivial or well-specified tasks where the overhead of N attempts adds no signal.

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

The arena proceeds with N-1 candidates and records the dropout in the synthesis note. The remaining candidates are still read end to end, scored against the rubric, and used for picking and grafting.

Why do parallel candidates sometimes wildly diverge in approach?▼

Wild divergence means the framing phase was under-specified: the shared prompt lacked a clear artifact definition or concrete success criteria. The fix is to reframe the prompt and re-run the arena rather than averaging the divergent outputs.