Pattern Detection

When to use this skill

• Code review: Proactively detect problematic patterns

• Security review: Scan for vulnerability patterns

• Refactoring: Identify duplicate code

• Monitoring: Alert on anomalies

Instructions

Step 1: Detect code smell patterns

Detect long functions:

Find functions with 50+ lines

grep -n "function|def|func " **/*.{js,ts,py,go} |
while read line; do file=$(echo $line | cut -d: -f1) linenum=$(echo $line | cut -d: -f2) # Function length calculation logic done

Duplicate code patterns:

Search for similar code blocks

grep -rn "if.==.null" --include=".ts" . grep -rn "try\s{" --include="*.java" . | wc -l

Magic numbers:

Search for hard-coded numbers

grep -rn "[^a-zA-Z][0-9]{2,}[^a-zA-Z]" --include="*.{js,ts}" .

Step 2: Security vulnerability patterns

SQL Injection risks:

SQL query built via string concatenation

grep -rn "query.+.$|execute.%s|query.f"" --include=".py" . grep -rn "SELECT.+.||" --include=".{js,ts}" .

Hard-coded secrets:

Password, API key patterns

grep -riE "(password|secret|api_key|apikey)\s*=\s*['"][^'"]+['"]" --include="*.{js,ts,py,java}" .

AWS key patterns

grep -rE "AKIA[0-9A-Z]{16}" .

Dangerous function usage:

eval, exec usage

grep -rn "eval(.)|exec(.)" --include="*.{py,js}" .

innerHTML usage

grep -rn "innerHTML\s*=" --include="*.{js,ts}" .

Step 3: Code structure patterns

Import analysis:

Candidates for unused imports

grep -rn "^import|^from.import" --include=".py" . |
awk -F: '{print $3}' | sort | uniq -c | sort -rn

TODO/FIXME patterns:

Find unfinished code

grep -rn "TODO|FIXME|HACK|XXX" --include="*.{js,ts,py}" .

Error handling patterns:

Empty catch blocks

grep -rn "catch.{[\s]}" --include="*.{js,ts,java}" .

Ignored errors

grep -rn "except:\spass" --include=".py" .

Step 4: Data anomaly patterns

Regex patterns:

import re

patterns = { 'email': r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}', 'phone': r'\d{3}[-.\s]?\d{4}[-.\s]?\d{4}', 'ip_address': r'\d{1,3}.\d{1,3}.\d{1,3}.\d{1,3}', 'credit_card': r'\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}', 'ssn': r'\d{3}-\d{2}-\d{4}', }

def detect_sensitive_data(text): found = {} for name, pattern in patterns.items(): matches = re.findall(pattern, text) if matches: found[name] = len(matches) return found

Statistical anomaly detection:

import numpy as np from scipy import stats

def detect_anomalies_zscore(data, threshold=3): """Z-score-based outlier detection""" z_scores = np.abs(stats.zscore(data)) return np.where(z_scores > threshold)[0]

def detect_anomalies_iqr(data, k=1.5): """IQR-based outlier detection""" q1, q3 = np.percentile(data, [25, 75]) iqr = q3 - q1 lower = q1 - k * iqr upper = q3 + k * iqr return np.where((data < lower) | (data > upper))[0]

Step 5: Trend analysis

import pandas as pd

def analyze_trend(df, date_col, value_col): """Time-series trend analysis""" df[date_col] = pd.to_datetime(df[date_col]) df = df.sort_values(date_col)

# Moving averages
df['ma_7'] = df[value_col].rolling(window=7).mean()
df['ma_30'] = df[value_col].rolling(window=30).mean()

# Growth rate
df['growth'] = df[value_col].pct_change() * 100

# Trend direction
recent_trend = df['ma_7'].iloc[-1] > df['ma_30'].iloc[-1]

return {
    'trend_direction': 'up' if recent_trend else 'down',
    'avg_growth': df['growth'].mean(),
    'volatility': df[value_col].std()
}

Output format

Pattern detection report

Pattern Detection Report

Summary

• Files scanned: XXX
• Patterns detected: XX
• High severity: X
• Medium severity: X
• Low severity: X

Detected patterns

Security vulnerabilities (HIGH)

Code smells (MEDIUM)

Recommended actions

1. [Action 1]
2. [Action 2]

Best practices

• Incremental analysis: Start with simple patterns

• Minimize false positives: Use precise regex

• Check context: Understand the context around a match

• Prioritize: Sort by severity

Constraints

Required rules (MUST)

• Read-only operation

• Perform result verification

• State the possibility of false positives

Prohibited (MUST NOT)

• Do not auto-modify code

• Do not log sensitive information

References

• Regex101

• OWASP Cheat Sheet

• Code Smell Catalog

Examples

Example 1: Basic usage

Example 2: Advanced usage