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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
    • Generated the document

    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.

    trading-mistake-pattern-detector.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    What you get

    Detect recurring execution errors like FOMO entries or early profit taking.Quantify the P&L impact of specific rule-breaking behaviors.Track improvement in discipline and process over multiple trading periods.Identify correlations between emotional states and risk management failures.

    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.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Verified clean 5 days ago

    • Passed all security checks, Safe to install

    Listed5 days ago

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