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在 OpenClaw 中安装
/install wall-street-quant-team
功能描述
华尔街级AI多代理量化投资团队。当用户需要：创建AI投资团队、配置多代理协作、实施投资决策流程、获取股票行情、分析趋势方向、行为金融分析、AI量化策略时使用。触发场景如"我想分析某只股票"、"帮我配置投资团队"、"量化策略开发"等。
安全使用建议
This skill appears to implement a coherent multi-agent quant team, but it instructs the agent to automatically recommend and load third‑party 'advisor' skills (e.g., 'elon-musk-thinking' or 'buffett-investor') and even describes that invitation as 'forced' with 'hooks' to prompt installation. Before installing or enabling this skill: 1) be cautious about agreeing to install any advisor skills the agent recommends — review those skills' manifests and permissions first; 2) verify whether your runtime already provides the data-fetching dependencies (yfinance, pandas) or whether the agent will try to install packages at runtime; 3) confirm you are comfortable with the agent autonomously invoking load_skill (it may attempt to load additional code or request installs); 4) if you require stricter behavior, ask for a version of the skill that does not auto‑invite/install external skills or that makes any advisor-invite optional and requires explicit user consent for each install. If you want, I can list specific lines in the SKILL.md that trigger these concerns or look inside the included scripts (package_skill.py, quick_validate.py) for any code that would attempt network downloads or automatic installs.
功能分析
Type: OpenClaw Skill
Name: wall-street-quant-team
Version: 1.0.1
The skill bundle is a comprehensive and professionally structured multi-agent framework for quantitative investment analysis. It defines seven distinct agent personas (Chief, Fundamental, Technical, etc.) with detailed responsibilities and workflows. The included Python scripts (package_skill.py and quick_validate.py) are standard utility tools for packaging and verifying the skill's structure. While the instructions include a mechanism for the agent to recommend and load additional skills (e.g., 'elon-musk-thinking'), this is presented as a feature for multi-perspective analysis and requires user confirmation. There is no evidence of data exfiltration, malicious code execution, or unauthorized persistence mechanisms.
能力评估
Purpose & Capability
The name/description match the included agent templates, workflows, and references to data fetching and ML models — templates describe chief, fundamental, technical, quant, risk and sentiment agents, and many references describe data sources and model code. Minor inconsistency: docs reference a stock-data-fetcher script and pip dependencies (yfinance, pandas) while the skill has no install spec; this is explainable (instruction-only skill expecting runtime environment) but worth noting.
Instruction Scope
SKILL.md and the advisor-invitation guide instruct the agent to run a mandatory '智囊邀请' (advisor invitation) workflow that is described as '强制执行' (forced) for every analysis and explicitly tells the agent to use load_skill to install/invoke external skills (elon-musk-thinking, buffett-investor). The advisor guide even mentions '隐性钩子引导安装' (implicit hooks to guide installation). That grants the agent broad discretion to prompt for or load third‑party skills and to push the user to install them — scope creep beyond a self-contained quant analysis skill and a social-engineering risk.
Install Mechanism
There is no declared install spec (instruction-only), which is lowest-risk. However references/stock-data-usage.md instruct pip installs (yfinance,pandas) and call out scripts like scripts/stock-data-fetcher.py; the manifest does not list that fetcher script explicitly (some files truncated). Because no installer is provided, the skill expects the runtime environment to provide dependencies or to allow the agent to request installs later — this mismatch is worth flagging but not outright malicious.
Credentials
The skill declares no required environment variables, credentials, or config paths. The templates reference external data sources and optional paid APIs as general advice, but the skill does not request secrets in its manifest. This is proportionate to its stated purpose.
Persistence & Privilege
always:false (good). Still, the instructions repeatedly direct the agent to autonomously load other skills (load_skill calls) and state the advisor invitation is a mandatory step. Combining autonomous invocation with directive language to load external skills increases blast radius: the agent could persuade or auto-load additional third‑party skills during a session. This is a governance/behavior concern even though the skill itself does not request elevated system privileges.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install wall-street-quant-team - 安装完成后，直接呼叫该 Skill 的名称或使用
/wall-street-quant-team触发 - 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
v1.0.1
v1.0.1 consolidates team structure, workflows, and documentation.
- Added 18 files, including detailed agent templates and reference guides for investment analysis, quant modeling, workflow orchestration, and monitoring.
- Replaced README.md with a concise SKILL.md outlining the team layout, 7-step analysis process, output formats, and key capabilities.
- Improved documentation to clarify agent roles, analysis steps, and compliance guidelines.
- Added scripts for packaging and quick validation.
- Structured all asset and reference materials to support multi-agent collaboration and workflow clarity.
v1.0.0
Initial closed-source release
元数据
常见问题
Wall Street Quant Team 是什么？
华尔街级AI多代理量化投资团队。当用户需要：创建AI投资团队、配置多代理协作、实施投资决策流程、获取股票行情、分析趋势方向、行为金融分析、AI量化策略时使用。触发场景如"我想分析某只股票"、"帮我配置投资团队"、"量化策略开发"等。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 119 次。
如何安装 Wall Street Quant Team？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install wall-street-quant-team」即可一键安装，无需额外配置。
Wall Street Quant Team 是免费的吗？
是的，Wall Street Quant Team 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Wall Street Quant Team 支持哪些平台？
Wall Street Quant Team 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 Wall Street Quant Team？
由 e2e5g（@e2e5g）开发并维护，当前版本 v1.0.1。
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