Select Super Stock

A 股/港股/美股优质股票筛选专家。擅长筛选具备中长期布局价值的优质股票，精准规避游资炒作和庄家控盘的垃圾股。基于两大核心模型：(1) 长线稳步上涨型（如中国海油）- 月线年线多头排列、ROE 高于 15%、高股息；(2) 历史低位反弹型（如万华化学）- 核心资产大幅回撤后企稳、行业周期见底。使用场景：用户询问某...

This package appears to implement the advertised stock-screener logic, but it has a number of practical/incoherent issues you should resolve before running it: - Missing helper files: the wrapper (scripts/run_with_cache.py) expects a script named stock_analyzer_orig.py and imports cache_utils from a _shared directory; those files/directories are not present in the manifest. Confirm where cache_utils and the original analyzer are supposed to come from. - Dependency management: the code requires Python packages (akshare, pandas). The skill provides no install instructions. Install dependencies in a controlled environment (virtualenv) using: pip install akshare pandas (and any other dependencies) and verify versions first. - Subprocess execution: run_with_cache uses subprocess.run to invoke another local script. If you add or rename files to satisfy the wrapper, verify the invoked script's content before running — subprocess will execute code on your machine. - Network access: AKShare fetches market data from external sources. Only install/run this in an environment where network access is acceptable and you trust the data sources. - Shebang/path assumptions: scripts use a Homebrew Python shebang path which may not exist on your system; run them with your python3 executable explicitly (e.g., python3 scripts/stock_analyzer.py --symbol 600938 --full). Recommended next steps before using: inspect/cache_utils (if provided elsewhere) and resolve the missing stock_analyzer_orig.py vs stock_analyzer.py mismatch; run the scripts in an isolated test environment (container or VM); review all script contents; and only then install dependencies and run. If you are not comfortable resolving these issues, treat this package as untrusted/unready.

Type: OpenClaw Skill Name: select-super-stock Version: 1.3.0 The 'select-super-stock' skill bundle is a legitimate stock analysis tool that implements technical and fundamental screening logic. The Python scripts (scripts/stock_analyzer.py and scripts/run_with_cache.py) use the standard 'akshare' library to fetch financial data and calculate indicators like MACD and RSI as described in the SKILL.md documentation. No evidence of data exfiltration, unauthorized network access, or malicious prompt injection was found; the code is well-structured and includes appropriate financial risk disclaimers.