| name | recursive-arena |
| description | Combine recursive outer-loop refinement with multi-model arena generation each round. Use when users request recursive arena, multi-LLM consensus with iterative refinement, or recursive plus model-battle workflows. |
| allowed-tools | Read, Bash(python:*) |
Recursive-Arena
Use the orchestrator:
python3 "${CLAUDE_PLUGIN_ROOT}/skills/recursive-arena/scripts/recursive_arena.py".
How to run
python3 "${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:
- Run
multi-model to generate a best candidate.
- Use judge summaries as critique input.
- Refine the prompt with the current best answer.
- 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.