CANSLIM-Top100-US
PassAudited by ClawScan on May 1, 2026.
Overview
This stock-screening skill appears purpose-aligned; it runs a local Python script that fetches public market data and writes a results file, with only minor dependency-install considerations.
This skill looks coherent for public stock screening. Before installing, consider using a Python virtual environment, review or pin the listed dependencies, and be aware that the script contacts Wikipedia/Yahoo Finance through yfinance and writes canslim_results.json in the skill directory.
Findings (2)
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.
Installing unpinned Python packages can pull newer or changed third-party code, though the packages are consistent with the stock-analysis purpose.
The dependency file lists packages without version pins or hashes; because SKILL.md instructs installing dependencies from this file if missing, the installed package versions may vary over time.
yfinance pandas lxml tqdm requests
Install in a dedicated virtual environment and consider pinning reviewed dependency versions before use.
Running the skill will contact external data providers and create or overwrite the expected local results file.
The script uses external network calls for market data and writes a generated JSON results file; these operations are disclosed by the skill and fit the stock-screening function.
response = requests.get(url, headers=headers) ... hist = stock.history(period="1y") ... json.dump(results_sorted, f, ensure_ascii=False, indent=4)
Run it only when network access to public market-data sources is acceptable, and check the generated results file before relying on the output.