All Weather Strategy
PassAudited by ClawScan on May 1, 2026.
Overview
This skill appears to be a purpose-aligned ETF allocation calculator, but users should note that it relies on external market-data packages and presents financial guidance that should be independently verified.
Before installing, understand that this skill calculates portfolio suggestions using external market data and third-party Python packages. It does not show evidence of credential use, trading, or malicious behavior, but you should run it in an isolated Python environment and verify any investment recommendation independently.
Findings (3)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
The skill may query external financial-data services while preparing the allocation report.
The engine contacts external market-data providers to fetch ETF price history. This is expected for a portfolio analysis tool, but users should know ETF symbols and date ranges are sent to third-party data services.
df = ak.fund_etf_hist_em(...); df = yf.download(tickers=yf_symbol, start=start_date, end=end_date, auto_adjust=True, progress=False)
Use only ETF symbols and parameters you are comfortable sending to market-data providers, and verify results against trusted financial sources.
Installing the skill could pull current versions of third-party packages rather than exactly reviewed versions.
The package list uses mostly unpinned dependencies, so future installations may resolve different package versions. This is not suspicious by itself, but it affects reproducibility and supply-chain review.
akshare yfinance numpy pandas matplotlib scipy reportlab streamlit>=1.42.0 altair>=5.0.0
Install in an isolated environment and consider pinning dependency versions if reproducibility or stricter supply-chain control is important.
Users may rely on generated portfolio allocations for real investment decisions.
The skill frames the assistant as a financial consultant and produces asset-allocation recommendations. This is aligned with the skill’s purpose, but users could over-trust outputs that are based on historical data and model assumptions.
The AI assistant must act as a financial consultant
Treat the output as informational analysis, not personalized financial advice, and consult qualified professionals before making investment decisions.