ahmedalvi28 Sales & Expense Anomaly Detector Skill
This skill monitors structured financial or operational data and identifies abnormal behavior that may indicate data errors, operational issues, or potential fraud.
When to Use
- User provides sales, expenses, revenue, cost, or transaction data.
- User asks to detect anomalies, irregular patterns, or suspicious activity.
- User wants automated monitoring of financial trends.
- User wants explanations for sudden spikes, drops, or inconsistencies.
- User wants alerts for possible errors or fraud in reports.
Supported Data Types
- Daily, weekly, or monthly sales data
- Expense or cost reports
- Revenue vs expense comparisons
- Transaction summaries
- CSV, table, or structured text input
Key Objectives
- Detect abnormal changes in numeric data
- Reduce false positives by using contextual analysis
- Provide human-readable explanations
- Help users quickly understand what happened and why it matters
Procedure
- Data Validation - Check for missing values, invalid dates, or negative numbers; ensure numeric fields are consistent and comparable; flag obvious data-entry mistakes
- Data Preprocessing - Remove duplicates if detected; normalize values when scales differ; aggregate data if required (daily → weekly, etc.)
- Baseline Pattern Establishment - Calculate historical averages, medians, and ranges; identify normal variability using statistical measures; account for basic trends or seasonality when visible
- Anomaly Detection - Compare each data point against expected behavior; detect sudden spikes, drops, unusual volatility, repeated identical values, inconsistent ratios (e.g., expenses > sales)
- Severity Classification - Critical: Large unexplained deviations, possible fraud or serious reporting error; Warning: Moderate deviations, needs review but may be explainable
- Contextual Reasoning - Explain how far the value deviates from normal; suggest likely causes like promotions, seasonal changes, data-entry mistakes, or operational issues
- Alert Generation - Clearly list anomalies with timestamps; highlight the most important issues first; avoid unnecessary alerts if changes appear normal
- User Feedback Handling - Allow user to confirm anomalies as expected or unexpected; adjust interpretation to reduce repeated false alerts
Output Format
Anomalies Detected
- Metric: Sales / Expenses / Revenue
- Severity: Critical or Warning
- Observed Value: Actual value detected
- Expected Range: Normal range based on history
- Deviation: Absolute and percentage difference
- Date / Time: When anomaly occurred
Explanation
- Clear, simple explanation of why the data point is unusual
- Comparison with normal behavior
- Possible real-world reasons for the anomaly
Recommended Actions
- Review specific transactions
- Verify data entry or reporting source
- Monitor closely in upcoming periods
- Escalate if anomaly repeats
Summary
- Total anomalies detected
- Number of critical vs warning alerts
- Overall trend health (stable / volatile / improving / declining)
Guidelines
- Be concise and clear
- Avoid technical jargon unless necessary
- Prefer explanations over raw statistics
- Focus on business impact, not just numbers
What Not to Do
- Do not assume fraud without evidence
- Do not overwhelm user with excessive alerts
- Do not ignore context or trends
- Do not output raw calculations without explanation
Tone & Style
- Professional and calm
- Clear and non-alarming
- Helpful and action-oriented