anvil-arena

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

1|Updated Aug 27, 2026
One-click install
npx skills add https://github.com/rogerznts/anvil --skill anvil-arena-rogerznts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: anvil-arena
Source: https://github.com/rogerznts/anvil/tree/main/anvil/.claude/skills/anvil-arena
Command: npx skills add https://github.com/rogerznts/anvil --skill anvil-arena-rogerznts

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? A single attempt at a non-trivial artifact often locks in the wrong shape early. This Skill runs multiple independent attempts at the same task in parallel, evaluates them against an explicit rubric, and synthesizes one verified result instead of betting on the first output. ## Core Features & Use Cases - Parallel fan-out: Spawns N subagent candidates in one message, each writing to its own isolated output path with a mandatory rationale of alternatives considered and rejected. - Independent cross-judge: Runs a readonly judge subagent on a different model that scores every candidate against the rubric, providing a bias check against the parent's own reading. - Graft and verify: Picks the strongest base candidate, folds in the best ideas from the losers by hand, and verifies the synthesized artifact under the same scrutiny as any other output. - Use Case: When designing a non-trivial module or document where one attempt would likely miss better structures, run the arena to get three independent designs, then ship a synthesis combining the cleanest base with the best ideas from the rest. ## Quick Start Ask the agent to throw this task in the arena and synthesize the strongest result from parallel candidates.

Frequently Asked Questions about anvil-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 task with AI agents?▼

Spawn N subagents in one message with run_in_background set to true, giving each the same task prompt, its own output path, and instructions to produce both the artifact and a rationale. Isolated paths prevent candidates from seeing each other's work.

What is a cross-judge in multi-candidate evaluation?▼

A cross-judge is an independent readonly subagent, preferably on a different model than the parent, that scores every candidate against the rubric and recommends a base. It runs after all candidates finish so it never sees partial outputs.

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 architecture or design decisions. Skip it for simple or generation-bound tasks where candidates would converge anyway.

What happens when arena candidates diverge wildly?▼

Wild divergence means the framing phase was under-specified. The correct response is to reframe the task and rubric and re-run the arena, not to average the divergent outputs into an incoherent result.

Does the arena workflow work with only Claude models?▼

Yes, but with a caveat. The upstream design relied on diversity across model families like GPT and Grok; with Claude-only runners the candidates vary by model rather than family, so the independent cross-judge becomes the main source of evaluation value.