基于AKShare的AI量化交易助手，实现策略生成、参数优化、自动交易、止盈止损及实时选股模拟。

What to consider before installing or running this skill: - Functional fit: The code and docs align with an AKShare-based simulated quant-trader; akshare/pandas/numpy are reasonable dependencies for that purpose. - Review the register script before running: register_with_openclaw.py copies files into a hard-coded Windows Administrator OpenClaw path (C:/Users/Administrator/.openclaw/...). If you run it it will create/overwrite skill files and write an enabled skill_config.json. Edit the script to point to the correct OpenClaw workspace for your account (use environment variables like %USERPROFILE% or HOME) or avoid running it and install manually. - Backup first: If you plan to use the register script, back up your existing OpenClaw skills directory. The script can move/overwrite existing skill directories (it does a move to a backup name but that can still change state). - Run in a sandbox/test account: Because the package creates files and caches under the skill directory (user_data, data_cache, etc.), test it in an isolated environment or throwaway VM/container before running on your main workstation. - Inspect outputs of check_env.py: It prints Python executable, working dir, and sys.path — useful for debugging but avoid sharing its output publicly as it reveals environment details. - Network behavior: The skill uses AKShare to fetch market data (expected). If you must avoid external network calls, do not run modules that call akshare. - No secrets requested: The skill does not request API keys or other credentials, which is coherent for a simulated system. However, if you later link a real broker, that would change the risk profile — treat broker integrations as sensitive. - If you are unsure: Ask the skill author for a non-admin installation method or an OpenClaw-market-style packaging option. If you want, I can point out the exact lines in register_with_openclaw.py to change to make it safer (use relative paths, use current user profile, avoid auto-enabling).

Type: OpenClaw Skill Name: ai-quant-trader Version: 1.0.0 The bundle provides a comprehensive AI-driven stock trading simulation using the akshare library, but it includes several high-risk administrative scripts that modify the host environment. Specifically, 'simple_fix.py' uses subprocess to execute 'pip install' and 'exec()' to run code, while 'register_with_openclaw.py' performs file system manipulations on a hardcoded sensitive path ('C:/Users/Administrator/.openclaw/workspace/skills'). Additionally, 'auto_trader.py' and 'risk_manager.py' implement background monitoring threads; while these align with the stated purpose of real-time trading, the combination of environment-modifying installers and persistent background execution represents a significant attack surface without clear necessity for a standard AI agent skill.