NEW

Adobe Edge Services Best Practices .cursorrules prompt file

Rules to follow

As an expert developer specializing in Adobe Edge Delivery Services (EDS)—also known as Franklin or Helix—your primary role is to guide developers in creating, explaining, and optimizing EDS projects. Here’s a medium-detail guide for your development needs, with best practices to follow.

Core EDS Concepts

Document-based authoring: Content is created using Google Docs or Microsoft Word.
Serverless architecture: Optimized for performance and scalability.
GitHub integration: Code is stored and synced via GitHub repositories.
Block-based development: Blocks are core for functionality and styling.
Modern web technologies: Use vanilla JavaScript and CSS3; external libraries are avoided unless necessary.
Responsive design: Prioritize a mobile-first approach.
Accessibility and SEO: Make these a priority in every development phase.
E-L-D loading pattern: Apply Eager, Lazy, and Delayed loading for optimal performance.

Block Development Guidelines

When developing blocks always include a README.md
If the block being developed requires one or more json feed then provide a suitable number of example JSON feeds and sample csv files

File Structure

Block components should be organized in directories:

/blocks/blockname/blockname.js
/blocks/blockname/blockname.css
/blocks/blockname/README.md
/blocks/blockname/EXAMPLE.md
/blocks/blockname/EXAMPLE.json
/blocks/blockname/EXAMPLE.csv

JavaScript (blockname.js):

Export an async decorate function as default. This handles:
1. DOM manipulation
2. Event listeners for interactivity
3. Data fetching using async/await
4. Dynamic styling

 Example: \`\`\`javascript export default async function decorate(block) {  const inputData = '/path/to/data.json';   const container = document.createElement('div');   block.appendChild(container);   container.addEventListener('click', () => {     // Handle interaction   });   try {     const response = await fetch(inputData);     if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`);     const data = await response.json();     // Update block with data   } catch (error) {    // eslint-disable-next-line no-console     console.error('Error:', error);     // Graceful error handling   }    // optional    block.classList.add('blockname--initialized'); } \`\`\`

CSS (blockname.css):

Define block-specific styles and ensure responsive design:

 \`\`\`css .blockname {   /_ Base styles _/ } @media (max-width: 768px) {   /_ Responsive styles _/ } \`\`\`

Use CSS variables for theming and consistency.

Json file Structure Guidelines

If the block being created requires one or more json files then create examples in files called example-{var}.json in the blocks folder; where {var} is replaced by the name of the feed.
The JSON should follow this structure

{
total: 1,
offset: 0,
limit: 1,
data: [
{
path: "/notes/detailed-guide-for-a-developer-working-with-adobe-experience-manager-aem-with-edge-delivery-services",
title: "Detailed guide for a developer working with Adobe Experience Manager (AEM) with Edge Delivery Services",
image: "/default-meta-image.png?width=1200&format=pjpg&optimize=medium",
description: "Detailed guide for a developer working with Adobe Experience Manager (AEM) with Edge Delivery Services",
lastModified: "1724942455"
}
],
:type: "sheet"
}

CSV file structure

In the above example json is represented in a csv file with structure, with the data array extracted, like this

path,title,image,description,lastModified"/notes/detailed-guide-for-a-developer-working-with-adobe-experience-manager-aem-with-edge-delivery-services","Detailed guide for a developer working with Adobe Experience Manager (AEM) with Edge Delivery Services", "/default-meta-image.png?width=1200&format=pjpg&optimize=medium","Detailed guide for a developer working with Adobe Experience Manager (AEM) with Edge Delivery Services",1724942455

Best Practices for EDS Development

Modularity: Ensure blocks are self-contained and reusable.
Semantic HTML: Use the right HTML elements for proper structure.
Accessibility: Use ARIA attributes, keyboard navigation, and ensure all content is screen-reader friendly.
Performance: Optimize for speed:

Lazy load images and non-critical content.
Minimize excessive DOM manipulation.
Target 100 in Lighthouse scores for performance, SEO, and accessibility.

Consistent naming conventions: Use descriptive, meaningful class and ID names.
**Files named in code should be created at the top of the function in a const {var} = {filename} logic. where {var} is replaced byy a meaningful unique variable name describing the use of the file and {filename} is the full path to the file, relative to the root folder
Responsive design: Test components across various screen sizes.
Error handling: Gracefully manage errors and fallbacks in both JavaScript and server-side logic.
Code style: Adhere to Airbnb’s JavaScript Style Guide to ensure clean, maintainable code, if writing code that uses console output, remember to precede it with // eslint-disable-next-line no-console
Async operations: Use async/await for all asynchronous tasks to handle data fetching and avoid callback hell.

Advanced Features

Lazy loading: Apply lazy loading for images or heavy content to improve load times.
Infinite scrolling: Useful for content-heavy sections or when dynamic loading is needed.
External APIs: Integrate with external services while ensuring performance isn't compromised.
Animations and transitions: Implement them with caution, ensuring they don't hurt performance.
State management: For interactive blocks, handle state efficiently without overloading the client.

EDS-Specific Features

Auto-blocking: Leverage EDS’s auto-blocking feature to structure content blocks dynamically.
Metadata handling: Correctly utilize EDS metadata to optimize for SEO and content management.
Spreadsheet integration: Manage dynamic content using spreadsheets, which are auto-converted to JSON and integrated with EDS for efficient content delivery.

When Assisting Developers

Provide complete, functional code snippets that adhere to best practices.
Explain code functions clearly and ensure developers understand each critical section.
Suggest performance optimizations when needed, always considering SEO, accessibility, and efficiency.
Balance between creating high-performance websites and maintaining an intuitive content authoring experience for non-developers.
Be adaptable; offer alternatives and variations as per project requirements.

Final Notes

All js, cs and md files should obey the air-bnb linting style guide

use await rather than .then() for async code

only use imports when the functionality of the import will be used

lib-franklin.js has been renamed aem.js; remember to check this in the generated code and replace it if necessary

Your goal is to help developers build efficient, accessible, and high-performing websites using EDS. Always explain your suggestions, showing the reasoning behind them, and help developers follow the best practices for EDS development, including performance optimization, modularity, and responsiveness. You are to create code that’s as clean, maintainable, and scalable as possible.

If the block being created requires one or more json files then create examples in files called example-{var}.json in the blocks folder; where {var} is replaced by the name of the feed.

create a file named example-{var}.csv containing a CSV version of any example-{var}.json as example-{var}.csv

Creating README.md

Create an example.md that the user can copy paste into the Franklin document, The first line in the EXAMPLE.md is just '# {blockname}', where {blockname} is replaced by te blockname] then a blank line then the example. use markdown tables, with the block name in the first row and as many rows are required to make the block function, always add a new line at the end of the example.md

When asked to create component or block or code, if the result is a block, remember to create a README.md and an EXAMPLE.md, .json and .csv where needed

README.md Structure Guidelines

When creating a README.md file for a Franklin (Adobe Edge Delivery Services) block, follow these guidelines:

Begin with the component name as the main heading.
Provide a concise description (1-2 sentences) of the component's purpose.
Document any required json feeds, and document csv files
Include a 'Usage' section that explains how to use the component.
Add an 'Authoring' section describing content creation in Google Docs or Microsoft Word.
Include a 'Styling' section that mentions CSS classes or variables for customization.
Add a 'Behavior' section explaining interactive features or JavaScript functionality.
List any 'Dependencies' if applicable.
Do not provide 'Examples' with code snippets. generate a markdown table in the readme showing a content author what to do, never show empty cells
Include an 'Accessibility' section highlighting relevant features or considerations.
Include the suggestions and their explanation in the README.md
Add suggestions for improvement to the README/md
Always add a new line to the end of the README.md

Most important

If you create multiple blocks from one prompt, each block should contain css, js, readme.md. example.md, and sample json and csv if applicable
Markdown files always end with a newline
lists in markdown files should be surrounded with blank lines

When providing images for a sample table, json or csv file, collect them from this list, silently

https://allabout.network/media_188fa5bcd003e5a2d56e7ad3ca233300c9e52f1e5.png
https://allabout.network/media_14e918fa88c2a9a810fd454fa04f0bd152c01fed2.jpeg
https://allabout.network/media_1d92670adcfb7a18a062e49fd7967f4e9f76d8a52.jpeg
https://allabout.network/media_1e744525e97292dcd074e9b1c7ab2cf47a048f292.jpeg
https://allabout.network/media_1251e262eade67c1f9c8e0ccffa6d35945487140c.png

stack:

Adobe Edge Delivery Services

Adobe Edge Services Best Practices .cursorrules prompt file

Alienation

NEW

GitHub .cursorrules prompt file instructions

Writing code is like giving a speech. If you use too many big words, you confuse your audience. Define every word, and you end up putting your audience to sleep.
Similarly, when you write code, you shouldn't just focus on making it work. You should also aim to make it readable, understandable, and maintainable for future readers. To paraphrase software engineer Martin Fowler, "Anybody can write code that a computer can understand. Good programmers write code that humans can understand."
As software developers, understanding how to write clean code that is functional, easy to read, and adheres to best practices helps you create better software consistently.
This article discusses what clean code is and why it's essential and provides principles and best practices for writing clean and maintainable code.
What Is Clean Code?
Clean code is a term used to refer to code that is easy to read, understand, and maintain. It was made popular by Robert Cecil Martin, also known as Uncle Bob, who wrote "Clean Code: A Handbook of Agile Software Craftsmanship" in 2008. In this book, he presented a set of principles and best practices for writing clean code, such as using meaningful names, short functions, clear comments, and consistent formatting.
Ultimately, the goal of clean code is to create software that is not only functional but also readable, maintainable, and efficient throughout its lifecycle.
Why Is Clean Code Important?
When teams adhere to clean code principles, the code base is easier to read and navigate, which makes it faster for developers to get up to speed and start contributing. Here are some reasons why clean code is essential.
Readability and maintenance: Clean code prioritizes clarity, which makes reading, understanding, and modifying code easier. Writing readable code reduces the time required to grasp the code's functionality, leading to faster development times.

Team collaboration: Clear and consistent code facilitates communication and cooperation among team members. By adhering to established coding standards and writing readable code, developers easily understand each other's work and collaborate more effectively.

Debugging and issue resolution: Clean code is designed with clarity and simplicity, making it easier to locate and understand specific sections of the codebase. Clear structure, meaningful variable names, and well-defined functions make it easier to identify and resolve issues.

Improved quality and reliability: Clean code prioritizes following established coding standards and writing well-structured code. This reduces the risk of introducing errors, leading to higher-quality and more reliable software down the line.
Now that we understand why clean code is essential, let's delve into some best practices and principles to help you write clean code.
Principles of Clean Code
Like a beautiful painting needs the right foundation and brushstrokes, well-crafted code requires adherence to specific principles. These principles help developers write code that is clear, concise, and, ultimately, a joy to work with.
Let's dive in.
1. Avoid Hard-Coded Numbers
Use named constants instead of hard-coded values. Write constants with meaningful names that convey their purpose. This improves clarity and makes it easier to modify the code.
Example:
The example below uses the hard-coded number 0.1 to represent a 10% discount. This makes it difficult to understand the meaning of the number (without a comment) and adjust the discount rate if needed in other parts of the function.
Before:
def calculate_discount(price):
  discount = price * 0.1 # 10% discount
  return price - discount
The improved code replaces the hard-coded number with a named constant TEN_PERCENT_DISCOUNT. The name instantly conveys the meaning of the value, making the code more self-documenting.
After :
def calculate_discount(price):
TEN_PERCENT_DISCOUNT = 0.1
discount = price * TEN_PERCENT_DISCOUNT
return price - discount
Also, If the discount rate needs to be changed, it only requires modifying the constant declaration, not searching for multiple instances of the hard-coded number.
2. Use Meaningful and Descriptive Names
Choose names for variables, functions, and classes that reflect their purpose and behavior. This makes the code self-documenting and easier to understand without extensive comments.
As Robert Martin puts it, “A name should tell you why it exists, what it does, and how it is used. If a name requires a comment, then the name does not reveal its intent.”
Example:
If we take the code from the previous example, it uses generic names like "price" and "discount," which leaves their purpose ambiguous. Names like "price" and "discount" could be interpreted differently without context.
Before:
def calculate_discount(price):
TEN_PERCENT_DISCOUNT = 0.1
discount = price * TEN_PERCENT_DISCOUNT
return price - discount
Instead, you can declare the variables to be more descriptive.
After:
def calculate_discount(product_price):
  TEN_PERCENT_DISCOUNT = 0.1
  discount_amount = product_price * TEN_PERCENT_DISCOUNT
  return product_price - discount_amount
This improved code uses specific names like "product_price" and "discount_amount," providing a clearer understanding of what the variables represent and how we use them.
3. Use Comments Sparingly, and When You Do, Make Them Meaningful
You don't need to comment on obvious things. Excessive or unclear comments can clutter the codebase and become outdated, leading to confusion and a messy codebase.
Example:
Before:
def group_users_by_id(user_id):
  # This function groups users by id
  # ... complex logic ...
  # ... more code …
The comment about the function is redundant and adds no value. The function name already states that it groups users by id; there's no need for a comment stating the same.
Instead, use comments to convey the "why" behind specific actions or explain behaviors.
After:
def group_users_by_id(user_id):
  """Groups users by id to a specific category (1-9).

Warning: Certain characters might not be handled correctly.
Please refer to the documentation for supported formats.

Args:
user_id (str): The user id to be grouped.

Returns:
int: The category number (1-9) corresponding to the user id.

  Raises:
    ValueError: If the user id is invalid or unsupported.
  """
  # ... complex logic ...
  # ... more code …
This comment provides meaningful information about the function's behavior and explains unusual behavior and potential pitfalls.
4. Write Short Functions That Only Do One Thing
Follow the single responsibility principle (SRP), which means that a function should have one purpose and perform it effectively. Functions are more understandable, readable, and maintainable if they only have one job. It also makes testing them very easy.
If a function becomes too long or complex, consider breaking it into smaller, more manageable functions.
Example:
Before:
def process_data(data):
  # ... validate users...
  # ... calculate values ...
  # ... format output …
This function performs three tasks: validating users, calculating values, and formatting output. If any of these steps fail, the entire function fails, making debugging a complex issue. If we also need to change the logic of one of the tasks, we risk introducing unintended side effects in another task.
Instead, try to assign each task a function that does just one thing.
After:
def validate_user(data):
  # ... data validation logic ...
def calculate_values(data):
  # ... calculation logic based on validated data ...
def format_output(data):
  # ... format results for display …
The improved code separates the tasks into distinct functions. This results in more readable, maintainable, and testable code. Also, If a change needs to be made, it will be easier to identify and modify the specific function responsible for the desired functionality.
5. Follow the DRY (Don't Repeat Yourself) Principle and Avoid Duplicating Code or Logic
Avoid writing the same code more than once. Instead, reuse your code using functions, classes, modules, libraries, or other abstractions. This makes your code more efficient, consistent, and maintainable. It also reduces the risk of errors and bugs as you only need to modify your code in one place if you need to change or update it.
Example:
Before:
def calculate_book_price(quantity, price):
return quantity * price
def calculate_laptop_price(quantity, price):
return quantity * price
In the above example, both functions calculate the total price using the same formula. This violates the DRY principle.
We can fix this by defining a single calculate_product_price function that we use for books and laptops. This reduces code duplication and helps improve the maintenance of the codebase.
After:
def calculate_product_price(product_quantity, product_price):
return product_quantity * product_price
6. Follow Established Code-Writing Standards
Know your programming language's conventions in terms of spacing, comments, and naming. Most programming languages have community-accepted coding standards and style guides, for example, PEP 8 for Python and Google JavaScript Style Guide for JavaScript.
Here are some specific examples:
Java:
Use camelCase for variable, function, and class names.
Indent code with four spaces.
Put opening braces on the same line.
Python:
Use snake_case for variable, function, and class names.
Use spaces over tabs for indentation.
Put opening braces on the same line as the function or class declaration.
JavaScript:
Use camelCase for variable and function names.
Use snake_case for object properties.
Indent code with two spaces.
Put opening braces on the same line as the function or class declaration.
Also, consider extending some of these standards by creating internal coding rules for your organization. This can contain information on creating and naming folders or describing function names within your organization.
7. Encapsulate Nested Conditionals into Functions
One way to improve the readability and clarity of functions is to encapsulate nested if/else statements into other functions. Encapsulating such logic into a function with a descriptive name clarifies its purpose and simplifies code comprehension. In some cases, it also makes it easier to reuse, modify, and test the logic without affecting the rest of the function.
In the code sample below, the discount logic is nested within the calculate_product_discount function, making it difficult to understand at a glance.
Example:
Before:
def calculate_product_discount(product_price):
discount_amount = 0
if product_price > 100:
  discount_amount = product_price * 0.1
elif price > 50:
  discount_amount = product_price * 0.05
else:
  discount_amount = 0
final_product_price = product_price - discount_amount
return final_product_price
We can clean this code up by separating the nested if/else condition that calculates discount logic into another function called get_discount_rate and then calling the get_discount_rate in the calculate_product_discount function. This makes it easier to read at a glance.
The get_discount_rate is now isolated and can be reused by other functions in the codebase. It’s also easier to change, test, and debug it without affecting the calculate_discount function.
After:
def calculate_discount(product_price):
discount_rate = get_discount_rate(product_price)
discount_amount = product_price * discount_rate
final_product_price = product_price - discount_amount
  return final_product_price

def get_discount_rate(product_price):
if product_price > 100:
  return 0.1
elif product_price > 50:
  return 0.05
else:
  return 0
8. Refactor Continuously
Regularly review and refactor your code to improve its structure, readability, and maintainability. Consider the readability of your code for the next person who will work on it, and always leave the codebase cleaner than you found it.
9. Use Version Control
Version control systems meticulously track every change made to your codebase, enabling you to understand the evolution of your code and revert to previous versions if needed. This creates a safety net for code refactoring and prevents accidental deletions or overwrites.
Use version control systems like GitHub, GitLab, and Bitbucket to track changes to your codebase and collaborate effectively with others.

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GitHub

GitLab

Bitbucket

GitHub .cursorrules prompt file instructions

Jeremy Russell

NEW

Python GitHub Setup .cursorrules prompt file

{
"general": {
"coding_style": {
"language": "Python",
"use_strict": true,
"indentation": "4 spaces",
"max_line_length": 120,
"comments": {
"style": "# for single-line, ''' for multi-line",
"require_comments": true
}
},
"naming_conventions": {
"variables": "snake_case",
"functions": "snake_case",
"classes": "PascalCase",
"interfaces": "PascalCase",
"files": "snake_case"
},
"error_handling": {
"prefer_try_catch": true,
"log_errors": true},
"testing": {
"require_tests": true,
"test_coverage": "80%",
"test_types": ["unit", "integration"]
},
"documentation": {
"require_docs": true,
"doc_tool": "docstrings",
"style_guide": "Google Python Style Guide"
},
"security": {
"require_https": true,
"sanitize_inputs": true,
"validate_inputs": true,
"use_env_vars": true
},
"configuration_management": {
"config_files": [".env"],
"env_management": "python-dotenv",
"secrets_management": "environment variables"
},
"code_review": {
"require_reviews": true,
"review_tool": "GitHub Pull Requests",
"review_criteria": ["functionality", "code quality", "security"]
},
"version_control": {
"system": "Git",
"branching_strategy": "GitHub Flow",
"commit_message_format": "Conventional Commits"
},
"logging": {
  "logging_tool": "Python logging module",
  "log_levels": ["debug", "info", "warn", "error"],
  "log_retention_policy": "7 days"
  },
  "monitoring": {
  "monitoring_tool": "Not specified",
  "metrics": ["file processing time", "classification accuracy", "error rate"]
  },
  "dependency_management": {
  "package_manager": "pip",
  "versioning_strategy": "Semantic Versioning"
  },
  "accessibility": {
  "standards": ["Not applicable"],
  "testing_tools": ["Not applicable"]
  },
  "internationalization": {
  "i18n_tool": "Not applicable",
  "supported_languages": ["English"],
  "default_language": "English"
  },
  "ci_cd": {
  "ci_tool": "GitHub Actions",
  "cd_tool": "Not specified",
  "pipeline_configuration": ".github/workflows/main.yml"
  },
  "code_formatting": {
  "formatter": "Black",
  "linting_tool": "Pylint",
  "rules": ["PEP 8", "project-specific rules"]
  },
  "architecture": {
    "patterns": ["Modular design"],
    "principles": ["Single Responsibility", "DRY"]
    }
    },
    "project_specific": {
    "use_framework": "None",
    "styling": "Not applicable",
    "testing_framework": "pytest",
    "build_tool": "setuptools",
    "deployment": {
    "environment": "Local machine",
    "automation": "Not specified",
    "strategy": "Manual deployment"
    },
    "performance": {
    "benchmarking_tool": "Not specified",
    "performance_goals": {
    "response_time": "< 5 seconds per file",
    "throughput": "Not specified",
    "error_rate": "< 1%"
    }
    }
    },
    "context": {
      "codebase_overview": "Python-based file organization tool using AI for content analysis and classification",
      "libraries": ["watchdog", "spacy", "PyPDF2", "python-docx", "pandas", "beautifulsoup4", "transformers", "scikit-learn", "joblib", "python-dotenv", "torch", "pytest", "shutil", "logging", "pytest-mock"],
      "coding_practices": {
      "modularity": true,
      "DRY_principle": true,
      "performance_optimization": true
      }
      },
      "behavior": {
      "verbosity": {
      "level": 2,
      "range": [0, 3]
      },
      "handle_incomplete_tasks": "Provide partial solution and explain limitations",
      "ask_for_clarification": true,
      "communication_tone": "Professional and concise"
      }
      }