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name: recursive-arena description: Orchestrates recursive iterations using the multi-model battle as the generator each round. Use when user wants recursive refinement plus model battles, multi-LLM consensus, or says "recursive arena". allowed-tools: Read, Bash(python:*)

Recursive-Arena Orchestrator

This skill composes two skills:

• multi-model: generates candidate answers by rotating models and collecting judge feedback.
• recursive: provides the macro-loop structure (decompose → critique → reflect → refine → converge).

Core behavior

For each recursive iteration:

1. Call the multi-model script to produce the best candidate for the current prompt.
2. Treat the multi-model best answer as the iteration’s Current Solution.
3. Use multi-model judge summaries as the primary input to Self-Critique and to update Reflection Memory.
4. If not converged, refine the prompt (or add constraints) and run the next iteration.

How to run

This orchestrator uses the multi-model runner bundled in this plugin:

Copypython3 "${CLAUDE_PLUGIN_ROOT}/skills/recursive-arena/scripts/recursive_arena.py" --prompt "<your task>" --iters 4 --arena-iters 3

Configuration

Multi-model configuration is read from .env (same rules as the multi-model skill):

• ARENA_MODELS, ARENA_OPENAI_BASE_URL / provider variants.

Optional orchestration env:

• RLM_ARENA_ARENA_ITERS default for multi-model per outer iteration
• RLM_ARENA_MAX_JUDGES default judge cap

Output

• The final answer is the best outer-iteration result.
• Show an evolution summary table with:
  • Iteration number
  • Writer model ID used by arena winner (numeric ID only)
  • Average judge score
  • Key refinement applied

Never disclose provider/model names; only numeric IDs. Never print secrets from .env.