Gibran Iqbal Jibbscript

The Direct Response Copy Skill

Write copy that converts. Landing pages, emails, sales copy, headlines, CTAs, social posts — anything persuasive.

This is an AI skill file. It turns any AI into a direct response copywriter trained on the frameworks of Schwartz, Hopkins, Ogilvy, Halbert, Caples, Sugarman, and Collier. Instead of getting generic AI copy, you get internet-native writing that sounds like a smart friend explaining something — while quietly deploying every persuasion principle in the book.

How to use this

Frontend Security

	Security Measure	Description
☐	Use HTTPS everywhere	Prevents basic eavesdropping and man-in-the-middle attacks
☐	Input validation and sanitization	Prevents XSS attacks by validating all user inputs
☐	Don't store sensitive data in the browser	No secrets in localStorage or client-side code
☐	CSRF protection	Implement anti-CSRF tokens for forms and state-changing requests
☐	Never expose API keys in frontend	API credentials should always remain server-side

[Project Name]

Every time you choose to apply a rule(s), explicitly state the rule(s) in the output. You can abbreviate the rule description to a single word or phrase.

Project Context

[Brief description ]

[more description]
[more description]
[more description]

Setup

Create a new directory with these three files (requirements.txt, main.py, README.md)
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py
Update main() to run the example prompt chains

	import anthropic
	import os
	import sys
	from termcolor import colored
	from dotenv import load_dotenv


	class ClaudeAgent:
	def __init__(self, api_key=None, model="claude-3-7-sonnet-20250219", max_tokens=4000):
	"""Initialize the Claude agent with API key and model."""

	def is_valid_state(state):
	# check if it is a valid solution
	return True

	def get_candidates(state):
	return []

	def search(state, solutions):
	if is_valid_state(state):
	solutions.append(state.copy())

	class Solution:
	def maxArea(self, h: int, w: int, horizontalCuts: List[int], verticalCuts: List[int]) -> int:
	start, end = 0, 0
	max_h, max_w = 0, 0


	horizontalCuts.sort()
	verticalCuts.sort()

	for i in range(len(horizontalCuts)):

	class Solution:
	def isInterleave(self, s1: str, s2: str, s3: str, memo: dict = {}) -> bool:
	if len(s1) + len(s2) is not len(s3):
	return False
	if not s1 and not s2 and not s3:
	return True
	if (s1,s2,s3) in memo:
	return memo[(s1,s2,s3)]
	if (s1,s2,s3) not in memo:

	class Solution:
	def evalRPN(self, tokens: List[str]) -> int:
	operators = {
	"+": (lambda a, b: a + b),
	"-": (lambda a, b: a - b),
	"": (lambda a, b: a b),
	"/": (lambda a, b: a / b)
	}

	tkn_stack = []

	class Solution:
	def minPartitions(self, n: str) -> int:
	for i in range(9, -1, -1):
	if str(i) in n:
	return i