A Share Multifactor Model
v1.0.0A股多因子模型/Barra风格因子分析。当用户说"多因子"、"multifactor"、"Barra"、"因子模型"、"风格因子"、"XX的因子暴露"、"因子收益率"、"风险模型"时触发。基于 cn-stock-data 获取行情和财务数据，构建多因子风险模型，分析因子暴露、因子收益、协方差矩阵。支持研报风格（f...
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by@yzswk
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included assets: SKILL.md explains workflows for factor construction and the repository includes a multifactor_builder.py that implements cross-sectional regression, factor statistics, and EWMA covariance. No unrelated binaries, credentials, or config paths are requested.
Instruction Scope
Runtime instructions tell the agent to invoke cn-stock-data scripts under $SKILLS_ROOT and then run the local multifactor_builder.py. This is consistent with the described workflow, but the SKILL.md assumes the presence of a separate cn-stock-data skill at $SKILLS_ROOT (not declared as a dependency here). The instructions do not read arbitrary system files or exfiltrate data to external endpoints.
Install Mechanism
No install spec is provided (instruction-only with one helper script). No external downloads or package installs are invoked by the skill itself.
Credentials
The skill declares no required environment variables or credentials. The code reads CSV inputs (returns/exposures) and performs local numeric computations; it does not request tokens, secrets, or unrelated environment access.
Persistence & Privilege
always is false and the skill does not modify agent configuration or request permanent presence. It runs as-needed and prints results to stdout; no privileged persistence behavior is present.
Assessment
This skill appears to do what it says: run local data-prep scripts and compute factor returns and covariances. Before installing, verify the following: 1) Confirm the referenced cn-stock-data scripts (under $SKILLS_ROOT/cn-stock-data) are trustworthy and inspect them for any network calls or credential usage, since SKILL.md assumes that dependency but doesn't declare it. 2) Ensure the CSV inputs you supply do not contain sensitive data you don't want processed. 3) Make sure required Python packages (pandas, numpy) are available in the environment. 4) Run the included multifactor_builder.py on test data in an isolated environment first to confirm outputs and that no unexpected I/O or network activity occurs.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.