Stock Theme Events

获取 A 股市场炒作题材对应的真实新闻事件。使用场景：(1) 分析近 N 日涨幅前列股票的题材分布，(2) 将相似题材聚类合并，(3) 从金十数据等新闻源获取对应题材的真实资讯事件，(4) 生成题材 - 事件关联分析报告。支持被其他 skill 调用或独立运行。

This skill appears to implement the described functionality, but take precautions before installing/running it: - Verify runtime requirements: the code calls an 'openclaw' CLI via subprocess to invoke ths-stock-themes; ensure you have (or intentionally want) that CLI available. The skill metadata did not declare this binary dependency. - Review and adjust file paths: run_full_analysis.py uses a hardcoded author-specific path (/Users/shinelp100/...), and scripts create cache and output files — change those paths to a safe workspace to avoid unexpected overwrites. - Expect large downloads: the sentence-transformers model (~500MB) will be fetched on first run if you install that dependency. - Network and automation: the skill uses akshare (network calls) and may direct a browser automation tool to scrape Jin10/other sites; only run in an environment where outbound network and web automation are acceptable. - Trust boundary: the skill spawns another skill (ths-stock-themes). Inspect or vet that other skill before allowing subprocess-based invocations, since its output is parsed and used here. If you plan to use this skill, run it in a sandboxed environment (or inspect/modify get_stock_themes.py to replace the subprocess call with a direct API or validated interface), update output paths, and ensure required Python packages and the 'openclaw' CLI are intentionally provided.

Type: OpenClaw Skill Name: stock-theme-events Version: 2.0.0 The skill bundle is classified as suspicious due to a vulnerability in 'scripts/get_stock_themes.py', where user-provided stock codes are directly interpolated into a natural language task string for a sub-agent ('openclaw sessions spawn'), creating a vector for indirect prompt injection. Additionally, 'scripts/run_full_analysis.py' contains hardcoded absolute file paths (e.g., '/Users/shinelp100/...'), which indicates poor portability and potential for unexpected behavior. While the core logic for stock theme clustering and news searching (using akshare and jin10.com) aligns with the stated purpose, the lack of input sanitization when constructing agent tasks is a significant security flaw in an autonomous agent environment.