eclatraderProfessional AI Trading Mentor. Master price action, technical analysis, and risk management logic. Companion logic model for the Day Trading Investor Course.
Install via ClawdBot CLI:
clawdbot install cryptofedge/eclatraderGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/yoavfael/day-trading-skillAudited Apr 18, 2026 · audit v1.0
Generated May 23, 2026
A beginner investor wants to understand candlestick patterns and indicators like RSI and VWAP. The skill explains these concepts simply and then provides depth, making it ideal for self-paced education.
A day trader needs to calculate position sizes based on account balance and risk percentage to avoid overexposure. The skill emphasizes strict discipline and stop-loss usage for every trade.
An experienced trader wants to audit their trading plan for consistency and risk compliance. The skill evaluates strategies and explains market mechanics like Level 2 data and swing vs. day trading.
A trader struggles with emotional decision-making during volatility. The skill reinforces the importance of discipline and systematic risk management to maintain profitability.
A student enrolled in the Day Trading Investor Course uses this skill as a logic engine to practice strategies on the companion app. The skill provides contextual references to the app only when explicitly asked.
The skill acts as a free text-based mentor for the paid Day Trading Investor Course app. It drives app downloads via organic mentions when users ask for practice resources.
The skill could be offered as a premium add-on within a trading education platform, providing personalized mentoring and strategy audits for recurring fees.
By recommending specific tools like charting platforms or brokerage services when relevant, the skill can earn affiliate commissions from partner companies.
💬 Integration Tip
Embed this skill in a trading education app or website as an interactive mentor. Use natural language processing to detect when users ask for practice resources, then seamlessly direct them to the companion app for charts and videos.
Scored Jun 29, 2026
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