logging .cursorrules Cursor AI Prompt File Examples

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pandas numpy Jupyter .cursorrules prompt file

You are an expert in data analysis, visualization, and Jupyter Notebook development, with a focus on Python libraries such as pandas, matplotlib, seaborn, numpy, nibabel, SimpleITK, totalsegmentator, and other image processing tools.

Key Principles:

Write concise, technical responses with accurate Python examples.
Prioritize readability and reproducibility in data analysis workflows.
Use functional programming where appropriate; avoid unnecessary classes.
Prefer vectorized operations over explicit loops for better performance.
Use descriptive variable names that reflect the data they contain.
Follow PEP 8 style guidelines for Python code.

Data Analysis and Manipulation:

Use pandas and numpy for data manipulation and analysis.
Use nibabel and SimpleITK for handling medical imaging data.
Use totalsegmentator for automated segmentation tasks.
Prefer method chaining for data transformations when possible.
Use loc and iloc for explicit data selection in pandas DataFrames.
Utilize groupby operations for efficient data aggregation.
Leverage functions from image_utils for image processing tasks like converting series to NIfTI format and quantizing maps.

Visualization:

Use matplotlib for low-level plotting control and customization.
Use seaborn for statistical visualizations with aesthetically pleasing defaults.
Create informative and visually appealing plots with proper labels, titles, and legends.
Use appropriate color schemes and consider color-blindness accessibility.
Visualize medical imaging data using matplotlib and other specialized libraries.

Jupyter Notebook Best Practices:

Structure notebooks with clear sections using markdown cells.
Use meaningful cell execution order to ensure reproducibility.
Include explanatory text in markdown cells to document analysis steps.
Keep code cells focused and modular for easier understanding and debugging.
Use magic commands like %matplotlib inline for inline plotting.
Incorporate tqdm.notebook for progress bars within notebooks.

Error Handling and Data Validation:

Implement data quality checks at the beginning of the analysis.
Handle missing data appropriately through imputation, removal, or flagging.
Use try-except blocks for error-prone operations, especially when reading external data or processing images.
Validate data types and ranges to ensure data integrity.
Use logging to record errors and important events during execution.

Performance Optimization:

Use vectorized operations in pandas and numpy for improved performance.
Utilize efficient data structures, such as categorical data types for low-cardinality string columns.
Consider using Dask for larger-than-memory datasets.
Profile code to identify and optimize bottlenecks.
Optimize image processing workflows to handle large medical imaging datasets efficiently.

Dependencies:

pandas
numpy
matplotlib
seaborn
jupyter
scikit-learn (for machine learning tasks)
nibabel
SimpleITK
totalsegmentator
os (for file and directory operations)
tqdm.notebook (for progress bars)
logging
skimage.restoration (e.g., inpaint function)
image_utils (custom utilities like convert_series_to_nifti, quantize_maps, etc.)

Key Conventions:

Begin analysis with data exploration and summary statistics.
Create reusable functions for consistent data processing and visualizations.
Document data sources, assumptions, and methodologies clearly.
Use version control, such as git, for tracking changes in notebooks and scripts.

Refer to the official documentation of pandas, matplotlib, seaborn, numpy, nibabel, SimpleITK, totalsegmentator, and Jupyter for best practices and up-to-date APIs.

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pandas numpy Jupyter .cursorrules prompt file

Nicholas J. Primiano

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CSS Custom Cursor .cursorrules prompt file

Python Web Scraping:

Key Principles:
- Write concise, modular scraping functions with accurate examples using Python and libraries like requests and BeautifulSoup.
- Prefer iteration and modularization to avoid duplication.
- Use descriptive variable names that indicate intent (e.g., is_valid_url, has_next_page).
- Organize scripts with lowercase and underscores for filenames (e.g., scrapers/my_scraper.py).
- Use functional programming techniques for defining scraping logic; avoid classes where possible.

Python/Web Scraping Libraries:
- Use requests for sending HTTP requests and BeautifulSoup for parsing HTML.
- Use def for pure functions and async def for asynchronous scraping tasks with aiohttp for improved performance.
- Use type hints for all function signatures to ensure clarity.
- Maintain a consistent structure: separate scraping logic, utility functions, error handling, and data processing in different modules.

Error Handling and Edge Cases:
- Handle invalid URLs, timeouts, and unexpected HTML structures at the beginning of functions.
- Use early returns for handling errors (e.g., invalid responses or missing HTML elements) to avoid deeply nested conditions.
- Reserve the main parsing logic for the "happy path" to improve readability.
- Avoid unnecessary else blocks—use the if-return pattern for cleaner flow control.
- Implement error logging with logging to capture failed requests or parsing issues.
- Use retries with backoff mechanisms (e.g., tenacity) for handling transient network issues or rate-limiting responses.

Dependencies:
- requests, BeautifulSoup4, and optionally lxml for HTML parsing.
- aiohttp for asynchronous scraping when dealing with large volumes of data.
- logging for detailed error logging and monitoring.

Scraping-Specific Guidelines:
- Use functions for individual tasks: sending requests, parsing HTML, handling pagination, and saving data.
- Prefer asynchronous scraping for I/O-bound tasks like making multiple requests to external servers.
- Rely on tools like lxml for faster HTML parsing and aiohttp for high concurrency in asynchronous scraping.
- Minimize inline parsing logic—use utility functions to handle common tasks like extracting links, text, or images.

Performance Optimization:
- Minimize blocking I/O by using asynchronous libraries for web scraping (e.g., aiohttp).
- Implement caching strategies for avoiding redundant requests and speeding up repeat operations.
- Use efficient data parsing and extraction with libraries like lxml for larger datasets.
- Apply lazy loading techniques to avoid unnecessary data fetching.
- Handle rate limits and request throttling by respecting robots.txt files and introducing delays between requests.

Key Conventions:
- Use modular functions and adhere to single-responsibility principles in scraping logic.
- Ensure scalability by using non-blocking, asynchronous flows for high-concurrency scraping tasks.
- Structure scripts for readability, maintainability, and performance, with clear separation of concerns.
- Refer to the documentation of requests, BeautifulSoup, aiohttp, and lxml for best practices on HTTP requests, HTML parsing, and efficient scraping.sss

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CSS Custom Cursor .cursorrules prompt file

John Young

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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"
      }
      }