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nickemmons

Robonet

作者 nickemmons · GitHub ↗ · v0.1.0
cross-platform ⚠ suspicious
1720
总下载
0
收藏
3
当前安装
1
版本数
在 OpenClaw 中安装
/install robonet-workbench
功能描述
Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions.
安全使用建议
This skill appears to do what it says (build/backtest/deploy trading strategies) but it does not declare how it will authenticate or bill for operations that require wallet access and money. Before installing: 1) Ask the publisher (or registry owner) for the authentication flow — what credentials are required, where private keys live, and whether you must provide them. 2) Confirm billing: who pays LLM/mcp costs and how payments/credits are charged. 3) Do not provide private keys or long-lived secrets unless you verify the server, source code, and privacy/billing policies. 4) Ask for a public homepage/repo or contact and for proof the MCP server is operated by a trusted party. 5) If you test, use read-only operations and very small test funds; require explicit, manual approval before any production deployment or wallet interaction. Because this is instruction-only, there is no code to audit locally — that increases the importance of external provenance and clear credential handling.
功能分析
Type: OpenClaw Skill Name: robonet-workbench Version: 0.1.0 This skill bundle is classified as suspicious due to its inherent high-risk capabilities, specifically the ability to deploy live trading agents that manage real funds on platforms like Hyperliquid via the `deployment_create` tool, and the ability to generate and modify Python code for trading strategies using tools like `create_strategy` and `refine_strategy`. While these capabilities are central to the skill's stated purpose of building and deploying trading strategies, they represent a significant potential for financial harm if misused by a malicious user prompt or if the agent is compromised. The `SKILL.md` and `shared-references/tool-catalog.md` files clearly document these powerful, but risky, functionalities.
能力评估
Purpose & Capability
The name/description (build/backtest/deploy trading strategies) matches the listed MCP tools and workflows in SKILL.md. However, several capabilities (deploying to Hyperliquid, managing vaults/wallets, viewing credit balance, integrating with Allora) inherently require authentication, wallet credentials, or billing configuration — none of which are declared in the skill metadata (no required env vars, no primary credential, no homepage/source). That omission is a coherence gap.
Instruction Scope
SKILL.md is an instruction-only integration that tells the agent to load and call MCP tools (data access, AI generation, backtesting, deployment). The instructions do not ask the agent to read local files, environment variables, or unrelated system paths. However, they do instruct potentially high-impact actions (create live deployments, view/modify strategies and strategy code). Because the skill can retrieve and return complete Python strategy source code, there is potential to handle sensitive secrets in those artifacts — SKILL.md does not document how such secrets are protected or whether the MCP server will ever request private keys.
Install Mechanism
No install spec and no code files — instruction-only skill. This minimizes local disk/write risk and there are no downloaded artifacts for the static scanner to analyze.
Credentials
The skill needs (or implies the need for) credentials and payment/billing info to perform several core actions (deployments, Hyperliquid vaults require wallet funding, account credit balance checks, LLM-cost billing). Yet requires.env is empty and no primary credential is declared. This is disproportionate: a trading/deployment skill should clearly document required API keys, wallet auth method (e.g., signing via user wallet, platform-managed keys), and billing arrangements.
Persistence & Privilege
always:false (good). The skill allows autonomous model invocation (default), which is normal; combined with the missing-auth issue, autonomous calls that trigger deployments or billing increase risk if credentials are handled unexpectedly. The skill does not request persistent system-wide privileges in its metadata.
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 robonet-workbench
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /robonet-workbench 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
v0.1.0
Initial release of robonet-workbench: a comprehensive toolkit for building, testing, and deploying trading strategies using Robonet's MCP server. - Provides 24 MCP tools across 6 categories: data access, AI strategy generation, backtesting, prediction markets, deployment, and account management. - Supports end-to-end workflows: data exploration, strategy creation (including ML-enhanced and AI-generated), backtesting, optimization, and live deployment on Hyperliquid/Polymarket. - Includes documentation for tool pricing, usage recommendations, strategy development best practices, and workflow examples. - Designed for crypto and prediction market traders leveraging Allora Network ML signals and Robonet's advanced automation.
元数据
Slug robonet-workbench
版本 0.1.0
许可证
累计安装 3
当前安装数 3
历史版本数 1
常见问题

Robonet 是什么?

Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1720 次。

如何安装 Robonet?

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

Robonet 是免费的吗?

是的,Robonet 完全免费(开源免费),可自由下载、安装和使用。

Robonet 支持哪些平台?

Robonet 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 Robonet?

由 nickemmons(@nickemmons)开发并维护,当前版本 v0.1.0。

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