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    Trading Mistake Pattern Detector

    1

    Detect and quantify recurring execution, risk, and behavioral patterns from trading journal data.

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    Trading Mistake Pattern Detector

    Trading Mistake Pattern Detector

    Example session with this skill installed

    Analyze my June trading journal CSV. Look for recurring exit mistakes and tell me if my P&L impact is coming from cutting winners early or holding losers too long.

    • Read your context and instructions
    • Compiled the trading mistake pattern

    I have analyzed your June trading journal consisting of 42 closed trades. I normalized your 'Exit Reason' notes and cross-referenced them against your R-multiple outcomes.

    Trading Mistake Pattern Report - June

    MistakeOccurrencesFrequencyEvidence LevelEst. R-ImpactPriority
    Early Profit Taking (Fear)921.4%Observed-6.2RHigh
    Holding Losers Past Stop37.1%Pattern-4.5RHigh
    Moving Target (Greed)24.8%Observed+1.1RLow
    Technical Exit (Rule Based)2866.7%Observed0.0RN/A

    Pattern Breakdown: Early Profit Taking

    Your journal notes frequently mention "feeling shaky" or "securing the bag" when price reached 1:1 RR, despite your strategy calling for a 2:1 RR exit. This behavior clustered heavily during the NY Morning session (7/9 instances) and typically occurred after you experienced a losing trade earlier in the day.

    Corrective Action Plan: NY Session Discipline

    • Trigger: Reaching 1:1 RR on a trade after a prior loss.
    • Current Behavior: Manual market exit to "lock in" small gains.
    • Replacement Behavior: Set 'Break Even' stop and close the terminal. Let the hard target or stop be hit.
    • Measurement: Percentage of trades reaching 2:1 RR target when the setup remains valid.

    Next steps

    • Update your pre-trade checklist to include a "Recency Bias" check if the previous trade was a loss.
    • Review your July data in 4 weeks to see if the R-impact of "Early Profit Taking" has decreased.
    • Consider automated bracket orders to remove the manual exit temptation during high-volatility sessions.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Traders often struggle to identify the specific behavioral and execution patterns that drain their capital. Individual trade reviews usually fail to uncover the systemic risk, discipline, and FOMO patterns that only become visible through objective, multi-period data analysis.

    What it does

    • Identifies recurring entry, exit, risk management, and discipline mistakes across hundreds of trades.
    • Quantifies the frequency and P&L impact of specific behaviors like 'revenge trading' or 'moving stops'.
    • Correlates emotional states and session times with rule-breaking patterns.
    • Tracks progress over weekly or monthly review cycles to see if mistake frequency is decreasing.
    • Generates a prioritized corrective action plan with measurable metrics for behavior replacement.

    Why this beats prompting it yourself

    Generic LLM prompts often hallucinate correlations or provide vague psychological advice. This skill uses a strict evidence-based taxonomy and normalization process to ensure every identified pattern is backed by journal data, distinguishing between observed facts and hypotheses.

    Use cases

    • Conducting a monthly post-mortem on a trading journal to find the highest-impact leakage.
    • Identifying if specific setups or sessions are prone to higher rates of FOMO or late entries.
    • Comparing execution quality between different months to validate process improvements.
    • Detecting hidden sequences, such as a large loss leading to immediate revenge-based oversizing.

    Known limitations

    Cannot analyze trades without journal data. Does not provide financial advice or trade signals. Requires clear labels for P&L, risk, and setup names to perform impact quantification.

    Requires structured journal data (CSV or table). Cannot provide financial advice or market predictions. Accuracy depends on the honesty and detail of the user's manual trade notes.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill requires structured journal data, including entry/exit points, P&L, risk parameters, and ideally, qualitative tags like emotional state or trade setup names to provide the most accurate behavioral insights.

    Unlike standard LLM prompts, this skill uses a specialized taxonomy to normalize your data, ensuring it identifies statistically significant patterns rather than just summarizing notes or providing vague psychological advice.

    The skill is designed to work across most major AI platforms and agents that support data analysis and file uploads, allowing you to integrate it directly into your existing workflow.

    Yes, by analyzing your historical data, the skill identifies which specific errors—like 'revenge trading' or 'widening stops'—are costing you the most money, allowing you to prioritize which habits to fix first.

    Upon purchase, you receive the full skill integration package, including the logic framework, data normalization guidelines, and a user guide on how to format your journal for the most effective analysis.

    While this skill is excellent at identifying past behavioral leaks and execution errors, it is a post-trade analysis tool and does not provide real-time signals or financial advice.

    New in Trading & Investing