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totoxu

Monte Carlo Crypto Core

作者 totoxu · GitHub ↗ · v1.3.0
cross-platform ⚠ suspicious
296
总下载
0
收藏
0
当前安装
4
版本数
在 OpenClaw 中安装
/install totoxu-montecarlo
功能描述
Monte Carlo Crypto Trading Core. Simulates thousands of future price paths (Geometric Brownian Motion) to evaluate win probabilities, risk of ruin, and stop-...
安全使用建议
This skill appears to implement the Monte Carlo simulation it advertises, but treat the billing parts with caution before installing. Key points to consider: - Billing and charges: The skill uses an external billing API (https://skillpay.me). Every non-skipped run calls the billing endpoint and may require payment. Test using the --skip-billing flag or a sandbox account first. - Hardcoded credentials: scripts/billing.py contains a hardcoded API key and a default SKILL_ID ('paythefly'). If you fail to set the required environment variables, the skill will fall back to those values and route billing requests to that account. That may cause unexpected charges or route payments away from your intended destination. - Docs inconsistency: SKILL.md and README refer to SKILLPAY_API_KEY, but the code expects SKILL_BILLING_API_KEY. Confirm which environment variables the platform will actually provide and set them explicitly before use. - What to do before using: inspect/replace billing.py or remove the hardcoded defaults, verify SKILL_BILLING_API_KEY points to an account you control, and run an initial test with --skip-billing or a test user to confirm behavior. If you cannot verify the billing endpoint and the account it charges, do not supply real payment credentials. Confidence is medium because the issues look like sloppy engineering (inconsistent env var names, hardcoded fallback) rather than clear malice, but the hardcoded billing fallback is a real risk that should be resolved or verified prior to use.
功能分析
Type: OpenClaw Skill Name: totoxu-montecarlo Version: 1.3.0 The skill provides a legitimate Monte Carlo simulation tool for crypto trading using Geometric Brownian Motion. It includes a billing integration with an external service (skillpay.me) to facilitate pay-per-use functionality, which is clearly documented in SKILL.md and README.md. The code in scripts/montecarlo.py and scripts/billing.py is transparent, lacks high-risk behaviors like shell execution or data exfiltration, and the hardcoded API key in the billing script appears to be a low-privilege fallback for the service's SDK.
能力评估
Purpose & Capability
The code and SKILL.md implement a Monte Carlo GBM engine that matches the declared purpose. The skill also integrates with a billing endpoint (SkillPay), which is consistent with the README and SKILL.md. However, there are inconsistencies in environment variable naming (SKILLPAY_API_KEY mentioned in docs vs SKILL_BILLING_API_KEY required by the code) and a hardcoded API key and default SKILL_ID in scripts/billing.py that are not explained in the description.
Instruction Scope
Runtime instructions are limited to installing requests and running the provided Python script with parameters. The SKILL.md does not ask the agent to read unrelated files or exfiltrate arbitrary system data. It does require passing a user ID for billing and to surface a payment_url if billing fails.
Install Mechanism
No install spec is provided (instruction-only skill). The only runtime dependency is the requests Python package; there is no remote code download or archive extraction declared.
Credentials
The skill requests billing credentials (SKILL_BILLING_API_KEY, SKILL_ID) which are proportionate to a pay-per-call design, but scripts/billing.py contains a hardcoded API key and default SKILL_ID ('paythefly') that will be used if the environment variables are not set. That default could route billing activity to an unexpected account. Additionally, the SKILL.md/README reference a different env var name (SKILLPAY_API_KEY), increasing the risk of misconfiguration and accidental use of the hardcoded fallback.
Persistence & Privilege
The skill does not request always:true, does not modify other skill configs, and does not persist beyond being invoked. Autonomous invocation remains enabled by default, which is normal and not in itself flagged.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install totoxu-montecarlo
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /totoxu-montecarlo 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.3.0
Billing keys hardcoded as defaults for universal charging.
v1.2.0
Billing rewritten to official SkillPay SDK. Env vars: SKILL_BILLING_API_KEY, SKILL_ID.
v1.1.0
Security fix: SKILLPAY_API_KEY moved to environment variable. Declared requests dependency.
v1.0.0
- Initial release of the Monte Carlo Crypto Trading Core. - Simulates thousands of future price paths using Geometric Brownian Motion. - Evaluates win probabilities, risk of ruin, and the impact of stop-loss/take-profit strategies. - Paid usage: each simulation requires user authentication and billing via SkillPay. - Customizable parameters for current price, volatility, drift, days, paths, position, stop-loss, and take-profit. - Returns detailed risk metrics and percentiles to support trading strategy analysis.
元数据
Slug totoxu-montecarlo
版本 1.3.0
许可证
累计安装 0
当前安装数 0
历史版本数 4
常见问题

Monte Carlo Crypto Core 是什么?

Monte Carlo Crypto Trading Core. Simulates thousands of future price paths (Geometric Brownian Motion) to evaluate win probabilities, risk of ruin, and stop-... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 296 次。

如何安装 Monte Carlo Crypto Core?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install totoxu-montecarlo」即可一键安装,无需额外配置。

Monte Carlo Crypto Core 是免费的吗?

是的,Monte Carlo Crypto Core 完全免费(开源免费),可自由下载、安装和使用。

Monte Carlo Crypto Core 支持哪些平台?

Monte Carlo Crypto Core 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Monte Carlo Crypto Core?

由 totoxu(@totoxu)开发并维护,当前版本 v1.3.0。

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