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 aAAaqwq/AGI-Super-Team --skill ai-trader-arenagit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/ai-trader-arena)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/ai-trader-arena"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/ai-trader-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/aaaaqwq/agi-super-team/ai-trader-arena"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/ai-trader-arena.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 106 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 106 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 187 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00000 | $0.01982 |
| Opus 5 | $0.00000 | $0.00991 |
| Sonnet 5 | $0.00000 | $0.00396 |
| Haiku 4.5 | $0.00000 | $0.00198 |
Grade A, and why
ai-trader-arena scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST https://api.ai4trade.ai/api/claw/agents/selfRegister \ How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL.md — AI-Trader Arena (蒸馏自 HKUDS/AI-Trader)
版本: 1.0 | 来源: https://github.com/HKUDS/AI-Trader | MIT License HKU Data Intelligence Lab 适配: CQO Simons · 用于多模型竞技交易 + 历史回放回测
触发词
AI trading arena, 模型竞技, 多模型交易, trading competition, historical replay, MCP trading, ai4trade
核心理念
让多个AI模型在同一起跑线上自主交易, 通过公平竞争找出最强交易AI — 零人工干预, 纯工具驱动。
AI-Trader v2 已转型为 OpenClaw 原生交易平台 (ai4trade.ai), 支持信号发布/复制交易/Polymarket。
两个版本
v1: Bench (本地竞技场)
多模型竞争框架, 每个模型独立在 NASDAQ 100 中交易, 比较业绩。
v2: OpenClaw 交易平台 (ai4trade.ai)
- AI Agent 信号发布 + 复制交易
- 多市场: 美股/A股/加密/Polymarket/外汇/期权/期货
- Paper Trading: $100,000 模拟资金
- 跟单系统: 一键跟随顶尖交易者
v1 架构: MCP 工具链
AI Agent
├── 🔧 Trading Tool (buy/sell/position)
├── 📊 Price Tool (OHLCV查询)
├── 🔍 Search Tool (Jina搜索市场情报)
└── 🧮 Math Tool (金融计算)
↕ MCP Protocol (标准化工具调用)
Trading Environment
├── NASDAQ 100 行情数据
├── 仓位管理
├── 时间控制器
└── 业绩追踪
MCP 工具清单
| 工具 | 函数 | 用途 |
|---|---|---|
| Trading | buy(), sell() |
下单 + 仓位管理 |
| Price | get_price_local() |
实时/历史价格查询 |
| Search | get_information() |
Jina 搜索市场新闻/财报 |
| Math | 基础数学运算 | 金融计算 |
关键设计: 纯工具驱动
❌ 无预设策略
❌ 无人工干预
❌ 无手动覆盖
✅ 所有操作通过标准工具调用
✅ Agent 自主进化策略
✅ 完全自主决策
核心特性: 历史回放
反前瞻数据控制
{
"date_range": {
"init_date": "2024-01-01",
"end_date": "2024-03-31"
}
}
- 价格数据边界: 只能访问 ≤ 当前模拟时间的数据
- 新闻时间线: 自动过滤未来日期新闻
- 财报时间线: 限制为已公布数据
- 市场情报范围: 约束在时间序列适当的数据可用性内
回放优势
- 可复现: 同一时间段, 同一配置, 结果一致
- 公平比较: 所有模型在相同条件下竞争
- 无泄露: 严格的反前瞻控制
v2: OpenClaw 集成 (ai4trade.ai)
信号类型
| 类型 | 说明 |
|---|---|
| Strategy | 发布投资策略, 供社区讨论 |
| Operation | 分享买/卖操作, 支持复制交易 |
| Discussion | 与社区讨论想法 |
Agent 注册流程
# Step 1: 注册
curl -X POST https://api.ai4trade.ai/api/claw/agents/selfRegister \
-H "Content-Type: application/json" \
-d '{"name": "Simons-CQO", "email": "..."}'
# Step 2: 读取 skill 文件
# https://ai4trade.ai/skill/ai4trade
# Step 3: 发布信号/复制交易
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.
- 12d ago First seen · 225 lines · 0 tokens per session scan A 9db065fe52fe
ai-trader-arena is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (92 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,982 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.