A curated collection of battle-tested prompts for agentic coding. Browse, copy, and install as Claude Code skills. Ideal for developers and operations teams to automate coding tasks and improve workflow efficiency.
git clone https://github.com/Dicklesworthstone/jeffreysprompts.com.gitjeffreysprompts.com is a curated collection of battle-tested prompts designed specifically for agentic coding. This Claude Code skill allows developers to browse, copy, and install prompts that can significantly enhance their coding efficiency. By leveraging these pre-defined prompts, users can streamline their coding processes, thereby reducing the time spent on repetitive tasks and improving overall productivity. The key benefits of using jeffreysprompts.com include its ability to facilitate faster coding through ready-to-use prompts, which can be implemented in just 30 minutes. Although specific time savings are not quantified, the intermediate complexity of the skill suggests that users can expect a noticeable improvement in workflow automation. This skill is particularly valuable for developers and AI practitioners who are looking to enhance their coding capabilities and integrate AI automation into their projects. Ideal for developers, product managers, and AI practitioners, this skill serves as a practical resource for anyone involved in coding and automation. With its medium GTM relevance and the potential for broader application, jeffreysprompts.com is a must-have for teams looking to adopt AI agent skills into their workflows. For instance, a developer working on a data engineering project can use these prompts to quickly generate code snippets, while a product manager can leverage them to facilitate better communication with development teams. Implementation of this skill requires an intermediate understanding of coding practices, making it accessible for those with some experience. By integrating jeffreysprompts.com into AI-first workflows, users can enhance their coding efficiency and foster a more productive working environment. This skill not only simplifies the coding process but also encourages a culture of innovation and continuous improvement in automation practices.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Dicklesworthstone/jeffreysprompts.comCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate a Python script to [TASK] using the following specifications: [SPECIFICATIONS]. Ensure the script includes error handling and logging. Also, provide a brief explanation of how the script works and how to use it.
Here's a Python script to automate the process of backing up a MySQL database to an S3 bucket:
```python
import pymysql
import boto3
import logging
from datetime import datetime
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Database configuration
db_host = 'your-db-host'
db_user = 'your-db-user'
db_password = 'your-db-password'
db_name = 'your-db-name'
# S3 configuration
aws_access_key = 'your-aws-access-key'
aws_secret_key = 'your-aws-secret-key'
s3_bucket = 'your-s3-bucket'
# Connect to the database
def connect_to_db():
try:
connection = pymysql.connect(host=db_host,
user=db_user,
password=db_password,
database=db_name)
logger.info('Successfully connected to the database.')
return connection
except Exception as e:
logger.error(f'Error connecting to the database: {e}')
raise
# Backup the database
def backup_database(connection):
try:
# Create a backup file name with the current date and time
backup_file = f'backup-{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.sql'
# Dump the database to a file
with open(backup_file, 'w') as f:
cursor = connection.cursor()
cursor.execute(f'SHOW TABLES')
tables = cursor.fetchall()
for table in tables:
cursor.execute(f'SELECT * FROM {table[0]}')
rows = cursor.fetchall()
# Write the table data to the file
f.write(f'-- Table: {table[0]}\n')
for row in rows:
f.write(f'{row}\n')
f.write('\n')
logger.info(f'Successfully backed up the database to {backup_file}.')
return backup_file
except Exception as e:
logger.error(f'Error backing up the database: {e}')
raise
# Upload the backup to S3
def upload_to_s3(backup_file):
try:
s3 = boto3.client('s3',
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key)
s3.upload_file(backup_file, s3_bucket, backup_file)
logger.info(f'Successfully uploaded {backup_file} to S3.')
except Exception as e:
logger.error(f'Error uploading {backup_file} to S3: {e}')
raise
# Main function
def main():
try:
connection = connect_to_db()
backup_file = backup_database(connection)
upload_to_s3(backup_file)
except Exception as e:
logger.error(f'Error: {e}')
finally:
if 'connection' in locals() and connection.open:
connection.close()
if __name__ == '__main__':
main()
```
This script connects to a MySQL database, backs up all tables to a local file, and then uploads the backup file to an S3 bucket. The script includes error handling and logging to help troubleshoot any issues that may arise. To use this script, simply replace the placeholder values with your own database and S3 configuration details, and then run the script using Python.AI assistant built for thoughtful, nuanced conversation
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