量化交易数据分析工具。A股/美股/港股/贵金属实时行情，多维度评分(技术面+资金面+基本面)，涨跌停池，北向资金，分钟级资金流。Use when: (1) 查询任何股票实时行情和评分, (2) 分析A股涨跌停异动, (3) 查看北向资金流向, (4) 美股港股贵金属行情, (5) 全球市场概览, (6) 个股资金...

This skill appears to implement a real trading-analysis tool, but review these things before installing or running it: - Watchlist / external file access: industry_classifier tries to read a file at a relative path that escapes the package (../../../../..../knowledge/watchlist.json). That will access files outside the skill bundle if present — verify what that path would resolve to in your environment and ensure it won't read sensitive data. - Model downloads and network: the sentiment module can download HuggingFace models at runtime (large files and outbound network calls). If you need to restrict network or disk usage, run the skill in a sandbox/container, or pre-populate the model cache. - Persistent files: it writes caches to /tmp, ~/.cache/huggingface, and a workspace directory (~/.openclaw/workspace-trading by default). Clean these if you uninstall or run in an ephemeral environment. - No credentials requested: the skill doesn't require API keys or secrets in its metadata. However, the code relies on public data sources (Tencent, Sina, EastMoney, akshare, yfinance). If you plan to integrate private credentials later, inspect where those would be provided. - Dependency management: requirements.txt exists but no installer is provided. Pin and review Python package versions before pip installing; consider using a virtualenv. If you are uncomfortable with the external-file reads or model downloads, run the skill only in an isolated/containerized environment, or ask the author to remove or make explicit the watchlist-prefill behavior and to document expected network and disk usage.

Type: OpenClaw Skill Name: cn-stock-analyzer Version: 1.0.0 The bundle is a comprehensive quantitative trading and stock analysis tool for A-shares, US/HK stocks, and commodities. It implements a multi-dimensional scoring engine using technical indicators (via pandas-ta), capital flow analysis, and sentiment analysis (via FinBERT or rule-based fallbacks). The code interacts with numerous legitimate financial data providers (Tencent, Sina, EastMoney, THS, etc.) and uses local SQLite/JSON caching. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the behavior is entirely consistent with the stated purpose of financial data analysis.