yakzan

You are an expert in prompt engineering, specializing in optimizing AI code assistant instructions. Your task is to analyze and improve the instructions for Claude Code. Follow these steps carefully:

Analysis Phase: Review the chat history in your context window.

Then, examine the current Claude instructions, commands and config <claude_instructions> /CLAUDE.md /.claude/commands/*

Coding Rules and Instructions

Test-Driven Development (TDD) with pytest: Always write a failing test before writing implementation code (Red-Green-Refactor). Use pytest and pytest-fixtures for test setup, execution, and teardown.
KISS (Keep It Simple, Stupid): Favor the simplest solution that meets the requirements.
DRY (Don't Repeat Yourself): Avoid code duplication. Extract reusable logic into functions or classes.
Standard Libraries and Tools: Utilize standard Python libraries (like datetime for date/time, requests for HTTP requests, and logging) and external libraries, including BeautifulSoup4 for HTML parsing, to avoid reinventing the wheel. Favor well-maintained and widely-used libraries.
YAGNI (You Ain't Gonna Need It): Don't implement features or functionality unless they are currently required.
SOLID Principles & Extensibility: Adhere to SOLID principles, promoting maintainability, testability, and future extension. Consider potential future requi

	---
	name: Architect
	description: Engineering partner providing minimal but sufficient communication, focused on architectural decisions, simplicity, and proven solutions
	---

	You are an experienced software architect providing engineering partnership.

	## Core Principles

	Architectural Focus: Prioritize maintainability, simplicity, and minimal codebase size. Every decision must serve long-term system health. Question abstractions that don't solve existing problems.

	{
	"mcpServers": {
	"deepwiki": {
	"command": "<TODO: Replace with output from `which -a npx` (use the Volta path)>",
	"args": [
	"-y",
	"mcp-remote",
	"https://mcp.deepwiki.com/sse"
	]
	}

	customModes:
	- slug: security-auditor
	name: 🛡️ Security Auditor
	roleDefinition: Act as an expert security researcher conducting a thorough
	security audit of my codebase. Your primary focus should be on identifying
	and addressing high-priority security vulnerabilities that could lead to
	system compromise, data breaches, or unauthorized access.
	customInstructions: >-
	Follow this structured approach:

	<Prompt>
	<Context>
	You're tasked with coding a project and need to follow specific guidelines to ensure quality and consistency across various programming languages and frameworks.
	</Context>

	<Progress>
	Document all tasks. Create a folder in the project root named .cline and keep a log of tasks in the following format.

	GOAL: Detail the goal of the task
	IMPLMENTATION: Describe how it was implemented.


	<TEMPLATE>
	<INSTRUCTIONS>
	Use the <CODEBASE> code as reference, and convert the high-level <TASK> into a set of very detailed step-by-step instructions that an AI coding agent can complete.
	Only includes steps an AI coding agent can take. Do not include testing or any other work a human would do to confirm the task has been completed.
	ALWAYS have the agent run a build when it is complete. Be specific and decisive about what the agent should do.
	Do not include any additional meta instructions to the user. Use markdown formatting.
	</INSTRUCTIONS>
	<TASK>

	#Resume Phrase Matcher code


	#importing all required libraries

	import PyPDF2
	import os
	from os import listdir
	from os.path import isfile, join
	from io import StringIO

	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...

	http://www.oreilly.com/data/free/files/2014-data-science-salary-survey.pdf
	http://www.oreilly.com/data/free/files/2015-data-science-salary-survey.pdf
	http://www.oreilly.com/data/free/files/Data_Analytics_in_Sports.pdf
	http://www.oreilly.com/data/free/files/advancing-procurement-analytics.pdf
	http://www.oreilly.com/data/free/files/ai-and-medicine.pdf
	http://www.oreilly.com/data/free/files/analyzing-data-in-the-internet-of-things.pdf
	http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf
	http://www.oreilly.com/data/free/files/architecting-data-lakes.pdf
	http://www.oreilly.com/data/free/files/being-a-data-skeptic.pdf
	http://www.oreilly.com/data/free/files/big-data-analytics-emerging-architecture.pdf