SkillsAdd

smithery/lollipopkit

recursive-arena

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.

Installation

$ npx skills add smithery/lollipopkit --skill recursive-arena
View on skills.sh ↗ Website ↗

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Also in this package

Other skills from smithery/lollipopkit.

npx skills add smithery/lollipopkit
use-fllib 0 recursive 0 Context Firewall 0

Browse all from smithery/lollipopkit

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Gemini CLI Not declared
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Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Bash(python:*)

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,549 B
  • docs SUMMARY.md 223 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Recursive-Arena

Use the orchestrator: python3 "${CLAUDEPLUGINROOT}/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:

  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
  • ARENAOPENAIBASEURL or ARENAPROVIDER<NAME>BASE_URL
  • Optional API keys (ARENAOPENAIAPIKEY, ARENAPROVIDER<NAME>API_KEY)

Optional orchestration env:

  • RLMARENAARENA_ITERS default inner arena iterations
  • RLMARENAMAX_JUDGES default judge cap

Output and Safety

  • Final answer is the best outer-iteration result.
  • When useful, show a compact evolution table:

- iteration - winnermodelid (numeric ID only) - avgjudgescore - refinement_applied

  • Never disclose provider/model names.
  • Never print secrets from .env.