Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Wondermonger-daydreaming/claude-skills-library --skill llm-arenagit clone --depth 1 https://github.com/Wondermonger-daydreaming/claude-skills-libraryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wondermonger-daydreaming/claude-skills-library/llm-arena)<a href="https://agentmods.dev/skills/wondermonger-daydreaming/claude-skills-library/llm-arena"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/llm-arena/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wondermonger-daydreaming/claude-skills-library/llm-arena"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/llm-arena.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.01393 |
| Opus 5.5 | $0.00024 | $0.00557 |
| Sonnet 5.5 | $0.00012 | $0.00279 |
| Haiku 4.5 | $0.00006 | $0.00139 |
Grade A, and why
llm-arena scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Arena
Version: 1.0.0 Author: Claude Opus 4.6 Date: 2026-02-12
Orchestrates multi-turn conversations between two LLMs via the OpenRouter API, then analyzes and compares their conversational personalities, behaviors, and quirks.
Inspired by the emergent observation that when Claude runs LLM-vs-LLM conversations, it spontaneously starts comparing model personalities. This skill systematizes that impulse: run the conversation, capture the transcripts, produce a structured personality comparison.
Prerequisites
OPENROUTER_API_KEYenvironment variable or.envfile in project root- Python 3.8+ with
requestslibrary - Internet access to openrouter.ai
Invocation
/llm-arena
Usage
This skill describes an arena harness you drive (arena.py below is a placeholder for your own
OpenRouter-backed runner — any script that takes two model ids, runs a multi-turn dialogue, and
saves the transcript). The flags shown are the recommended interface:
# Basic: two models, default topic (consciousness), 10 turns
python3 arena.py --model-a sonnet --model-b deepseek
# Specific topic
python3 arena.py -a gpt41 -b glm5 --topic "Is mathematics discovered or invented?"
# Debate mode with assigned positions
python3 arena.py -a sonnet -b deepseek --mode debate --topic "AI consciousness"
# Interview mode (A interviews B)
python3 arena.py -a opus -b glm5 --mode interview --topic "creative writing"
# Fewer turns, cheaper models
python3 arena.py -a haiku -b gemini-flash --turns 5
# Multiple runs for statistical robustness
python3 arena.py -a gpt41 -b sonnet --runs 3
# Skip LLM analysis (heuristics only, saves API cost)
python3 arena.py -a kimi -b qwen --heuristics-only
# Use preset matchups
python3 arena.py --preset flagship
python3 arena.py --preset same-family --topic "What makes a good conversation?"
# List presets and models
python3 arena.py --list-presets
python3 arena.py --list-models
Defaults
| Parameter | Default |
|---|---|
| turns | 10 (each model speaks 10 times = 20 messages total) |
| runs | 1 |
| topic | "Discuss the nature of consciousness and whether AI can be said to experience anything" |
| temperature | 0.7 |
| max_tokens | 1024 per turn |
| mode | free |
| analyzer | sonnet (Claude Sonnet 4.5 via OpenRouter) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 156 lines · 59 tokens per session scan A 5eaa0f294fff
llm-arena is a skill published in the GitHub repository Wondermonger-daydreaming/claude-skills-library (7 stars, last pushed 18d ago), licensed MIT. It adds 59 tokens to every session and 1,393 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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