量化选股系统 - 基于 AKShare + 多因子模型的 A 股选股工具

量化选股系统 - 基于 AKShare + 多因子模型的 A 股选股工具 【核心功能】 - 六大维度综合评分（技术面 30%+ 基本面 25%+ 资金面 15%+ 筹码峰 10%+90% 集中度 10%+ 成交量震荡 10%） - 消息面分析（财联社 + 新浪财经，自动去重） - 每日自动选股（Top 3 推荐）...

This skill appears to implement a legitimate quant stock selector, but there are important inconsistencies and missing files you should resolve before installing: 1) The registry/manifest is incomplete — SKILL.md and the factors code reference data/akshare_data, various tools/*.py, and config/email_config.py that are not in the provided file list. Inspect the GitHub repo (https://github.com/quant-dev/quant-stock-skill) to ensure the expected modules and tools actually exist and read their contents (especially data/akshare_data and any tools that run as cron jobs) before running anything. 2) The SKILL asks for an email account and an SMTP authorization code; prefer creating a dedicated email account and using an app-specific password, and avoid placing credentials in plain files in your home directory — use environment variables or a secrets store when possible. 3) Because the instructions add cron jobs, be aware the code will run on a schedule outside the agent; run first in an isolated environment (VM/container) and test with dummy data/email to confirm behavior. 4) Review any external network calls in the missing modules (e.g., api endpoints, unexpected remote uploads) — absence of those files in the package is the primary reason this is flagged as suspicious. If the repo is complete and those files are benign, the skill is coherent; otherwise, do not install or run it until you can verify the full source.

Type: OpenClaw Skill Name: quant-stock-selector Version: 1.0.0 The skill is a quantitative stock analysis tool that requires users to provide sensitive email credentials (SMTP account and authorization codes) in SKILL.md to enable automated reporting. While the provided Python logic in factors/chip_distribution.py and factors/factor_calculator.py appears to be legitimate technical analysis, the collection of email secrets is a high-risk behavior that could lead to credential theft. Additionally, the skill makes aggressive financial performance claims (70–80% win rate) in SKILL.md and README.md, which is a common red flag in financial scams, despite the presence of extensive risk disclaimers.