canslim-analysisExecutes a hybrid quantitative and qualitative CANSLIM analysis on US stocks using a fixed schema and a modular Python pipeline, returning a ranked shortlist.
Install via ClawdBot CLI:
clawdbot install lkmsteven/canslim-analysisGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://en.wikipedia.org/wiki/List_of_S%26P_500_companiesAudited Apr 16, 2026 · audit v1.0
Generated Mar 22, 2026
A retail investor wants to identify high-potential US growth stocks using the CANSLIM methodology. They need a systematic way to filter hundreds of stocks based on earnings growth, relative strength, and volume trends, then enrich with qualitative catalysts to build a focused watchlist.
A portfolio manager at a small fund needs to audit potential additions to their portfolio. They use this skill to run a reproducible analysis, ensuring each stock meets both quantitative metrics and qualitative checks like institutional quality and new catalysts before making investment decisions.
A financial advisor uses the skill to generate clear, ranked reports for clients, showing which CANSLIM criteria are met or missed. This helps in explaining investment recommendations based on a hybrid model of hard data and AI-enriched insights.
A trading desk at a brokerage firm runs the analysis daily to screen for breakout candidates in a confirmed uptrend. They rely on the modular pipeline to quickly update rankings based on latest price/volume data and AI-evaluated catalysts.
An investment club uses this skill to teach members about CANSLIM investing. They run analyses on practice portfolios, reviewing the intermediate and final JSON outputs to understand how quantitative and qualitative factors combine in stock selection.
A fintech startup integrates this skill into a subscription service where users pay monthly for access to CANSLIM analysis reports. The platform automates the pipeline, providing ranked shortlists and detailed criteria breakdowns to subscribers.
A data analytics company licenses this skill to financial institutions, embedding it into their internal tools for stock screening. They offer customization options and support, generating revenue through licensing fees and service contracts.
An investment app offers basic CANSLIM screening for free, with premium features like AI enrichment and advanced reports behind a paywall. This model attracts casual investors and upsells to serious traders seeking deeper insights.
💬 Integration Tip
Ensure all required Python files are in the same directory and verify the JSON schema compatibility before running the pipeline to avoid errors in data handoffs.
Scored Jun 19, 2026
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