llm-engineering-skills

🥈Silver

LLM Engineering Skills automates coding tasks for operations teams. It generates, reviews, and refactors code using Claude's AI capabilities. Integrates with GitHub, VS Code, and other developer tools to streamline workflows and reduce manual effort.

1200Updated 1w ago

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/itsmostafa/llm-engineering-skills.git

Works with:

Claude

Overview

About This Skill

The LLM Engineering Claude Skills focus on enhancing AI automation through advanced language model engineering. This skill allows developers and AI practitioners to integrate and optimize large language models (LLMs) into their workflows, facilitating more efficient and effective AI agent interactions. By leveraging these skills, users can streamline processes that involve natural language processing, making it easier to build intelligent applications that understand and generate human-like text. One of the key benefits of implementing LLM engineering skills is the significant improvement in workflow automation. While the exact time savings are not quantified, the intermediate complexity of this skill suggests that developers can expect to see a reduction in the time spent on repetitive tasks related to language processing. With a typical implementation time of just 30 minutes, this skill is designed to fit seamlessly into the busy schedules of product managers and AI practitioners who seek to enhance their capabilities without extensive downtime. This skill is particularly relevant for developers working in AI-focused roles, as well as product managers looking to integrate AI solutions into their products. It can also benefit teams in various departments that require advanced language processing capabilities, making it a versatile addition to any AI-first workflow. For example, customer support teams can utilize LLM engineering skills to automate responses to common inquiries, while content creation teams can enhance their writing processes by generating drafts or suggestions based on user input. Despite its intermediate difficulty, the LLM engineering skill is accessible to those familiar with AI concepts and programming. Users should have a basic understanding of machine learning principles and be comfortable working with code to fully leverage its capabilities. By incorporating this skill into their workflows, teams can not only improve efficiency but also enhance the overall quality of their AI-driven applications, positioning themselves for success in an increasingly competitive landscape.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/itsmostafa/llm-engineering-skills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm working on a [PROGRAMMING_LANGUAGE] project for [COMPANY] in the [INDUSTRY] sector. I need help with the following tasks: [TASK_1], [TASK_2], and [TASK_3]. Please generate clean, well-commented code and explain any complex logic. Also, review the following code snippet for potential improvements: [CODE_SNIPPET].

Example Output

# Code Generation and Review for [COMPANY] in the [INDUSTRY] Sector

## Task 1: Data Processing Script
```python
# Import necessary libraries
import pandas as pd

# Load the dataset
data = pd.read_csv('[DATASET_PATH]')

# Data cleaning
# Remove rows with missing values
data = data.dropna()

# Convert date column to datetime format
data['date'] = pd.to_datetime(data['date'])

# Add a new column for year
data['year'] = data['date'].dt.year

# Save the cleaned data
data.to_csv('[CLEANED_DATASET_PATH]', index=False)
```

## Task 2: Code Review

### Original Code:
```python
def calculate_average(numbers):
    total = 0
    for num in numbers:
        total += num
    average = total / len(numbers)
    return average
```

### Suggested Improvements:
1. **Input Validation**: Add a check to ensure the input list is not empty to avoid division by zero.
2. **Type Hints**: Add type hints to make the function signature clearer.
3. **Docstring**: Add a docstring to explain the function's purpose and parameters.
4. **Edge Cases**: Consider handling cases where the input is not a list of numbers.

### Improved Code:
```python
def calculate_average(numbers: list[float]) -> float:
    """
    Calculate the average of a list of numbers.

    Args:
        numbers: A list of numbers to calculate the average of.

    Returns:
        The average of the numbers.

    Raises:
        ValueError: If the input list is empty or contains non-numeric values.
    """
    if not numbers:
        raise ValueError("Input list cannot be empty")
    
    total = 0
    for num in numbers:
        if not isinstance(num, (int, float)):
            raise ValueError("All elements must be numeric")
        total += num
    
    return total / len(numbers)
```