Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
Quant
v1.0.0智能量化投资助手，支持多源数据获取、因子计算、多引擎回测、实时风控和交易信号推送。
⭐ 1· 723·8 current·9 all-time
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
The name/description (quantitative investment assistant) aligns with the included Python modules (data access and factor/alpha code). However the SKILL.md references additional modules (factors.py, backtest.py, risk.py) and CLI commands (quant setup, quant install, quant data, etc.) that are not present in the file manifest or registry metadata. That mismatch (advertised functionality vs. provided files) reduces confidence that the skill will behave as described.
Instruction Scope
SKILL.md instructs the agent to run CLI commands such as `quant setup` and `quant install` and promises to 'immediately create lib/data.py and config.yaml skeleton'. In this package the data.py and config.yaml already exist, but there is no provided CLI binary or wrapper in the manifest. The instructions also assert 'all data processed locally, no exfiltration' — there is no code enforcing this (the code fetches remote data via tushare/akshare/yfinance). The instructions are therefore vague and grant broad discretion to the agent (e.g., to install dependencies or create files) without a clear, reproducible runtime plan.
Install Mechanism
There is no install specification (instruction-only skill). That limits automatic installation risk, but SKILL.md tells the agent it will 'auto install dependencies' on `quant install` despite no install steps being declared. If the agent runs pip/apt/brew commands at runtime, it will perform network installs — a normal behavior for such a skill but one the user should be aware of since the install commands are not specified or reviewable in the manifest.
Credentials
The registry metadata declares no required environment variables, but lib/data.py reads os.getenv('TUSHARE_TOKEN') when attempting to call tushare.pro_api. config.yaml also contains a tushare_token field. This is a mismatch: the skill expects (or will behave differently with) a secret token but does not declare it in requires.env/primaryEnv. No other unrelated credentials are requested, but the missing declaration and the token dependency are noteworthy because the user may be prompted to supply credentials later.
Persistence & Privilege
always is false, there are no config paths requested, and the code does not attempt to modify other skills or system-wide agent settings. The skill does mention creating files and installing dependencies, but that is normal for a code-providing skill and is contained to its own files.
What to consider before installing
This skill looks like a legitimate quant helper but has several practical inconsistencies you should resolve before installing or providing credentials: (1) Ask the author or maintainer why TUSHARE_TOKEN (or config.tushare_token) is not declared in the registry metadata — do not paste your tushare token until you review the code. (2) Confirm how `quant install` / `quant setup` are implemented: there is no CLI wrapper in the manifest, so check what commands the agent will run to 'auto install' dependencies. (3) Request the missing modules referenced in SKILL.md (factors.py, backtest.py, risk.py) or a minimal reproducible install/run guide; current SKILL.md advertises capabilities not present in the package. (4) If you plan to use real trading (signal → execution), insist on explicit, auditable confirmation steps for any real-money operations. If you cannot verify these points, treat the skill as untrusted and avoid supplying API tokens or running automatic install commands.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.