a-c-m

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:

1. 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/*

sotayamashita

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

HighwayofLife

Coding Rules and Instructions

1. 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.
2. KISS (Keep It Simple, Stupid): Favor the simplest solution that meets the requirements.
3. DRY (Don't Repeat Yourself): Avoid code duplication. Extract reusable logic into functions or classes.
4. 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.
5. YAGNI (You Ain't Gonna Need It): Don't implement features or functionality unless they are currently required.
6. SOLID Principles & Extensibility: Adhere to SOLID principles, promoting maintainability, testability, and future extension. Consider potential future requi

iamhenry

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:

entrepeneur4lyf

<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.

kleneway

<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>

Venkatstatistics

#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

thomwolf

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...

nerzid

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

ashokpant

#!/bin/bash

# install CUDA Toolkit v9.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda-9-0