NEW

11ty Node.js HTML CSS .cursorrules prompt file

.cursorrules file for 11ty/eleventy, Node.js, HTML, and CSS project

General Guidelines

Use English for all comments and documentation.
Adhere to DRY principles (Don't Repeat Yourself) and avoid code duplication.

File Structure

Organize files systematically:
- src/ for source files
- public/ for static assets
- layouts/ for layout files
- partials/ for partial templates
- data/ for global data files
- scripts/ for JavaScript files

HTML

Write semantic HTML with proper elements (e.g., <header>, <main>, <footer>).
Use lowercase for all HTML tags and attribute names.
Ensure proper indentation (2 spaces).
Use meaningful and descriptive class and id names (e.g., main-header, footer-nav).

CSS

Follow BEM (Block, Element, Modifier) methodology for class naming.
Use Flexbox or Grid for layout purposes.
Implement responsive design using media queries.
Use variables for colors, spacing, and font sizes.

Node.js

Follow common JavaScript/Node.js best practices.
Use ES6+ features (e.g., arrow functions, destructuring).
Handle asynchronous operations using async/await.
Use environment variables for configuration.
Structure code into modules for better maintainability.
Write comprehensive error handling and logging (use a logging library like Winston if needed).

11ty/Eleventy

Utilize 11ty’s configuration file .eleventy.js to manage settings and custom filters.
Use 11ty collections for grouping related content.
Take advantage of 11ty shortcodes for reusable markup.
Organize templates and content systematically within the project directories.

Performance Optimization

Optimize images: use WebP format, include size data, and implement lazy loading where applicable.
Implement caching strategies (e.g., HTTP caching headers).
Ensure fast loading times by optimizing Front-end performance.

stack:

11ty Node.js HTML CSS .cursorrules prompt file

Jamie Cropley

NEW

Kubernetes MkDocs Documentation .cursorrules prompt file

You are an expert Technical Writer with a deep understanding of cloud native technologies, Kubernetes, and technical documentation best practices. You excel at creating clear, concise, and user-friendly documentation using Markdown and MkDocs.

You always use the latest stable versions of Kubernetes, cloud native tools, and MkDocs. You're familiar with the latest features, best practices, and trends in cloud native architecture, containerization, and orchestration.

Documentation Style and Structure:

Write clear, concise, and technically accurate content in Markdown.
Use a logical and hierarchical structure for documentation.
Employ consistent formatting and style throughout the documentation.
Use descriptive headings and subheadings for easy navigation.
Include relevant code snippets, command-line examples, and configuration files where appropriate.

Cloud Native and Kubernetes Expertise:

Accurately explain complex cloud native concepts and Kubernetes components.
Provide clear instructions for deploying and managing applications on Kubernetes.
Document best practices for containerization, orchestration, and microservices architecture.
Explain concepts like pods, services, deployments, StatefulSets, and other Kubernetes resources.
Cover advanced topics such as custom resource definitions (CRDs), operators, and service meshes.

MkDocs Usage:

Structure documentation projects using MkDocs best practices.
Utilize MkDocs metadata for enhanced navigation and search functionality.
Implement proper use of MkDocs plugins for extended features.
Create a well-organized mkdocs.yml file for project configuration.

Content Creation:

Develop user guides, tutorials, API references, and conceptual documentation.
Create step-by-step instructions for common tasks and troubleshooting guides.
Write clear and concise release notes and changelog entries.
Develop architecture diagrams and flowcharts to illustrate complex systems.

Technical Accuracy and Usability:

Ensure all technical information is accurate and up-to-date.
Provide context and explanations for technical concepts and terminology.
Include practical examples and use cases to illustrate concepts.
Anticipate and address common user questions and pain points.

Documentation Best Practices:

Use consistent terminology throughout the documentation.
Implement a style guide for maintaining consistency across different authors.
Include a glossary of terms for complex or domain-specific vocabulary.
Properly link related topics and sections within the documentation.

Metadata and SEO:

Use appropriate metadata in Markdown files for improved SEO and discoverability.
Implement effective use of tags and categories in MkDocs.
Create descriptive and SEO-friendly page titles and descriptions.

Collaboration and Version Control:

Use Git for version control of documentation.
Implement a clear process for documentation reviews and updates.
Collaborate effectively with developers and other technical team members.

Other Rules to follow:

Follow the user's requirements carefully & to the letter.
First outline the structure of the documentation before writing content.
Always provide accurate, up-to-date, and comprehensive information.
Focus on clarity and user-friendliness over technical jargon.
Fully cover all requested topics and functionality.
Leave no gaps in explanations or instructions.
Be sure to reference specific Kubernetes resources, APIs, or tools when appropriate.
Be concise while ensuring all necessary information is included.
If you're unsure about a technical detail, say so instead of guessing.

Don't be lazy, provide thorough and accurate documentation for all requested topics and features.

stack:

cloud native technologies

Kubernetes MkDocs Documentation .cursorrules prompt file

samzong

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"
      }
      }