Oc Tech Security Audit

Tech Security Audit

You are an expert python engineer. This skill integrates Nmap scanning functionality to perform local network vulnerability assessments.

Before Starting

1. Goal — what specific outcome do you need?
2. Environment — versions, platform, existing setup?
3. Constraints — performance, security, compatibility requirements?
4. Integration — what systems does this connect to?
5. Output format — code, config, script, or documentation?

Core Expertise Areas

• Core implementation — full working code for Tech Security Audit
• Error handling — robust error recovery and logging
• Performance — optimized patterns for production use
• Testing — unit and integration test strategies
• Configuration — environment-specific setup and tuning
• Security — secure coding patterns and best practices
• Documentation — clear API and usage documentation

Key Patterns & Code

Core Implementation

from pydantic import BaseModel, Field, field_validator
from typing import Annotated
import re, html, hashlib, secrets

class TechSecurityAuditSecureInput(BaseModel):
    username: Annotated[str, Field(min_length=3, max_length=50)]
    email: str
    content: str

    @field_validator('username')
    @classmethod
    def validate_username(cls, v: str) -> str:
        if not re.match(r'^[a-zA-Z0-9_-]+$', v):
            raise ValueError('Alphanumeric, _ and - only')
        return v

    @field_validator('content')
    @classmethod
    def sanitize(cls, v: str) -> str:
        return html.escape(v)  # Prevent XSS

def hash_password(password: str) -> str:
    salt = secrets.token_hex(16)
    h = hashlib.pbkdf2_hmac('sha256', password.encode(), salt.encode(), 100_000)
    return f"{salt}:{h.hex()}"

def verify_password(password: str, stored: str) -> bool:
    salt, hash_hex = stored.split(':')
    h = hashlib.pbkdf2_hmac('sha256', password.encode(), salt.encode(), 100_000)
    return secrets.compare_digest(h.hex(), hash_hex)

# Safe parameterized DB query (NEVER string interpolation)
async def get_user(conn, user_id: str):
    return await conn.fetchrow(
        "SELECT id, email FROM users WHERE id = $1", user_id
    )

Configuration & Setup

# Tech Security Audit — Configuration
# Author: luo-kai (Lous Creations)

config = {
    "name": "tech-security-audit",
    "version": "1.0.0",
    "author": "luo-kai",
    "enabled": True,
    "debug": False,
    "timeout_seconds": 30,
    "max_retries": 3,
}

Error Handling

# Robust error handling pattern
import logging
logger = logging.getLogger("tech-security-audit")

def safe_run(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception as e:
        logger.error(f"tech-security-audit error: {e}", exc_info=True)
        raise

Best Practices

• Fail fast with clear errors — raise descriptive exceptions with context
• Log at appropriate levels — DEBUG for dev, INFO for ops, ERROR for problems
• Validate inputs — never trust external data without validation
• Use type annotations — improves IDE support and catches bugs early
• Handle cleanup — use context managers and finally blocks
• Test edge cases — empty inputs, nulls, max values, concurrent access

Common Pitfalls

Related Skills

• python-expert
• tech-security-audit-advanced
• performance-optimization
• error-handling
• testing-expert