Overview
This prompt aims to guide users in creating a Python script for validating account data against configurable criteria. Programmers and developers will benefit by gaining a structured approach to automate account categorization and validation tasks.
Prompt Overview
Purpose: This script processes account data to categorize accounts based on defined criteria.
Audience: It is intended for developers and data analysts in the programming and gaming industries.
Distinctive Feature: The script uses a modular design for easy updates and clear JSON configuration for flexibility.
Outcome: The result is a structured folder with categorized account files and a summary of banned accounts.
Quick Specs
- Media: Text
- Use case: Generation
- Industry: Cryptocurrency & Blockchain, Development Tools & DevOps, Productivity & Workflow
- Techniques: Decomposition, Role/Persona Prompting, Structured Output
- Models: Claude 3.5 Sonnet, Gemini 2.0 Flash, GPT-4o, Llama 3.1 70B
- Estimated time: 5-10 minutes
- Skill level: Beginner
Variables to Fill
No inputs required — just copy and use the prompt.
Example Variables Block
No example values needed for this prompt.
The Prompt
Create a Python script that reads account data from a file named ‘accounts.txt’ and checks each account against criteria defined in a configurable JSON file. The script should:
– Read and parse account details from ‘accounts.txt’.
– Load configuration from a JSON file specifying:
– Skin names to check.
– Requirement thresholds (e.g., minimum level, minimum item count).
For each account, the script should:
– Determine account properties such as:
– Level
– Item count
– Special statuses (diamond, emerald, champ, platinum, go4, glacier)
– Validate whether the account is banned.
– Categorize accounts based on requirements:
– Valid level 50 accounts
– Valid accounts with items
– Valid diamond accounts
– Valid emerald accounts
– Valid champ accounts
– Valid platinum accounts
– Valid accounts that don’t meet any of the above requirements
– Valid go4 accounts
– Valid glacier accounts
The script must:
– Create a folder structure under ‘results/YYYY-MM-DD_HH-MM-SS/’.
– Save separate ‘.txt’ files for each category in the dated subfolder with appropriate account listings.
– Compile all banned accounts from any valid category into a file named ‘valid_banned.txt’ in the same folder.
– Ensure the JSON configuration is human-readable and editable with a clear structure for skins and requirement thresholds.
– Maintain a modular script structure for easy updates.
# Output Format
– A ‘results’ folder containing a timestamped subfolder (e.g., ‘results/2024-06-01_14-30-00/’)
– Multiple ‘.txt’ files inside the dated subfolder, one per account category as listed above
– A JSON configuration file for skins and requirements
# Notes
– Clearly define account property extraction logic based on the account data format.
– Make proxy format one-line if proxies are involved.
– Include comments for major functional parts in the script.
# Response Format
Provide a complete, well-commented Python script that fulfills these requirements.
Screenshot Examples
How to Use This Prompt
- Copy the prompt above.
- Open your preferred code editor.
- Create a new Python file and paste the prompt.
- Run the script to generate account reports.
- Check the ‘results’ folder for output files.
Tips for Best Results
- Read Account Data: Use Python’s built-in file handling to read ‘accounts.txt’ and parse account details into a structured format.
- Load JSON Configuration: Utilize the `json` module to load and parse the configuration file, defining skin names and requirement thresholds.
- Categorize Accounts: Implement logic to check each account against the defined criteria and categorize them accordingly, including handling banned accounts.
- Create Results Structure: Use `os` and `datetime` modules to create a timestamped folder structure for saving categorized account files and the banned accounts list.
FAQ
- What does the Python script read from 'accounts.txt'?
The script reads account details such as level, item count, and special statuses. - How does the script categorize accounts?
Accounts are categorized based on level, item count, and special statuses like diamond or emerald. - What files are created by the script?
The script creates separate '.txt' files for each account category and a 'valid_banned.txt' file. - Where does the script save the results?
Results are saved in a timestamped subfolder under the 'results' directory.
Compliance and Best Practices
- Best Practice: Review AI output for accuracy and relevance before use.
- Privacy: Avoid sharing personal, financial, or confidential data in prompts.
- Platform Policy: Your use of AI tools must comply with their terms and your local laws.
Revision History
- Version 1.0 (February 2026): Initial release.
