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:\s*pass" --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)
| File | Line |
how to use pattern-detection
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use pattern-detection on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add pattern-detection
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/supercent-io/skills-template --skill pattern-detectioncopy
The skills CLI fetches pattern-detection from GitHub repository supercent-io/skills-template and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/pattern-detection
Reload or restart Cursor to activate pattern-detection. Access the skill through slash commands (e.g., /pattern-detection) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?