recursive-arena

# Recursive-Arena Use the orchestrator: `python3 "${CLAUDE_PLUGIN_ROOT}/skills/recursive-arena/scripts/recursive_arena.py"`. ## How to run ```bash python3 "${CLAUDE_PLUGIN_ROOT}/skills/recursive-arena/scripts/recursive_arena.py" \ --prompt "<task>" --iters 4 --arena-iters 3 --json ``` Common flags: `--max-judges`, `--temperature`, `--max-tokens`, `--timeout`. ## Iteration Loop For each outer iteration: 1. Run `multi-model` to generate a best candidate. 2. Use judge summaries as critique input. 3. Refine the prompt with the current best answer. 4. Keep the global best by score and continue. ## Configuration Reuses `multi-model` `.env` configuration: - `ARENA_MODELS` - `ARENA_OPENAI_BASE_URL` or `ARENA_PROVIDER_<NAME>_BASE_URL` - Optional API keys (`ARENA_OPENAI_API_KEY`, `ARENA_PROVIDER_<NAME>_API_KEY`) Optional orchestration env: - `RLM_ARENA_ARENA_ITERS` default inner arena iterations - `RLM_ARENA_MAX_JUDGES` default judge cap ## Output and Safety - Final answer is the best outer-iteration result. - When useful, show a compact evolution table: - `iteration` - `winner_model_id` (numeric ID only) - `avg_judge_score` - `refinement_applied` - Never disclose provider/model names. - Never print secrets from `.env`.