structured-content-storage

name	structured-content-storage
description	Enforces structured, highly documented storage for code and data projects. Use when working on machine learning scripts, data processing, code creation, or script modification that should preserve clear structure and documentation.

Structured Content Storage Skill

Ensures all created or processed content follows strict organizational and documentation standards with structured storage, comprehensive comments, and complete project documentation.

When to Use This Skill

Use this skill for tasks like:

• Writing machine learning training scripts
• Creating data processing or data cleaning scripts
• Developing any code that processes or transforms data
• Modifying existing structured projects or scripts
• Creating analysis scripts or computational workflows
• Building data pipelines or ETL processes
• Any code creation task that produces files or processes data

Not For / Boundaries

• Pure conversational queries without code output
• Reading or analyzing existing code without modification
• Simple one-line fixes that don't affect project structure

Required inputs: If modifying existing projects, must first read and understand the original structure.

Quick Reference

Core Principles

1. Structured Directory Layout

project-name/
├── README.md                 # Project overview and directory guide
├── src/                      # Source code with detailed comments
│   ├── main.py              # Main entry point
│   └── utils.py             # Utility functions
├── data/                     # Data files
│   ├── raw/                 # Original data
│   ├── processed/           # Cleaned/transformed data
│   └── DATA_DICTIONARY.md   # Data field descriptions
├── docs/                     # Documentation
│   ├── PROCESS.md           # Step-by-step process description
│   └── CHANGELOG.md         # Modification history
├── outputs/                  # Results, models, reports
└── requirements.txt          # Dependencies

2. Code Documentation Standards

• Every function must have docstring explaining purpose, parameters, returns
• Complex logic must have inline comments explaining the "why"
• File headers must describe the file's purpose and main components
• Magic numbers must be explained or converted to named constants

3. Required Documentation Files

README.md must include:

• Project purpose and goals
• Directory structure explanation
• Setup and installation instructions
• Usage examples
• Dependencies

PROCESS.md must include:

• Step-by-step workflow description
• Data flow diagrams (text-based acceptable)
• Key decisions and rationale
• Expected inputs and outputs

DATA_DICTIONARY.md (for data projects) must include:

• Field name, type, description for each column
• Value ranges and constraints
• Data source and collection method
• Update frequency

CHANGELOG.md (for modifications) must include:

• Date and version
• What was changed and why
• Files affected
• Breaking changes or migration notes

4. Modification Protocol

When modifying existing structured projects:

1. Read and understand original structure
2. Maintain existing organizational patterns
3. Update all affected documentation
4. Add detailed entry to CHANGELOG.md
5. Update comments in modified code sections

Common Patterns

Pattern 1: ML Training Project Structure

ml-training-project/
├── README.md                 # Project overview
├── src/
│   ├── train.py             # Training script with detailed comments
│   ├── model.py             # Model architecture
│   ├── data_loader.py       # Data loading utilities
│   └── evaluate.py          # Evaluation metrics
├── data/
│   ├── raw/                 # Original datasets
│   ├── processed/           # Preprocessed data
│   └── DATA_DICTIONARY.md   # Feature descriptions
├── models/                   # Saved model checkpoints
├── logs/                     # Training logs
├── docs/
│   ├── TRAINING_PROCESS.md  # Training methodology
│   └── MODEL_ARCHITECTURE.md # Model design decisions
└── requirements.txt

Pattern 2: Data Cleaning Project Structure

data-cleaning-project/
├── README.md
├── src/
│   ├── clean.py             # Main cleaning script
│   ├── validators.py        # Data validation functions
│   └── transformers.py      # Transformation utilities
├── data/
│   ├── raw/                 # Original data
│   ├── processed/           # Cleaned data
│   ├── DATA_DICTIONARY.md   # Field descriptions
│   └── QUALITY_REPORT.md    # Data quality metrics
├── docs/
│   └── CLEANING_PROCESS.md  # Cleaning steps and rationale
└── requirements.txt

Pattern 3: Code Comment Template

"""
Module: data_processor.py
Purpose: Process and transform raw sensor data into analysis-ready format

Main components:
- DataLoader: Reads raw CSV files
- DataCleaner: Handles missing values and outliers
- DataTransformer: Applies normalization and feature engineering
"""

def clean_sensor_data(df, threshold=0.95):
    """
    Clean sensor data by removing outliers and handling missing values.

    Args:
        df (pd.DataFrame): Raw sensor data with columns [timestamp, sensor_id, value]
        threshold (float): Completeness threshold (0-1) for keeping sensors

    Returns:
        pd.DataFrame: Cleaned data with outliers removed and missing values imputed

    Process:
        1. Remove sensors with >5% missing data
        2. Detect outliers using IQR method (1.5 * IQR)
        3. Impute remaining missing values with forward fill
    """
    # Remove sensors with insufficient data
    # Threshold of 0.95 means sensor must have 95% valid readings
    completeness = df.groupby('sensor_id')['value'].count() / len(df)
    valid_sensors = completeness[completeness >= threshold].index
    df = df[df['sensor_id'].isin(valid_sensors)]

    # Detect and remove outliers using IQR method
    Q1 = df['value'].quantile(0.25)
    Q3 = df['value'].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR  # Standard outlier detection threshold
    upper_bound = Q3 + 1.5 * IQR
    df = df[(df['value'] >= lower_bound) & (df['value'] <= upper_bound)]

    # Forward fill remaining missing values
    # Assumes temporal continuity in sensor readings
    df = df.sort_values(['sensor_id', 'timestamp'])
    df['value'] = df.groupby('sensor_id')['value'].fillna(method='ffill')

    return df

Pattern 4: CHANGELOG.md Entry Template

## [Version 1.2.0] - 2026-01-19

### Changed
- Modified `train.py:45-67` to add early stopping mechanism
  - Reason: Prevent overfitting on small validation sets
  - Added `patience` parameter (default=10 epochs)
  - Monitors validation loss instead of training loss

### Added
- New function `evaluate.py:calculate_confusion_matrix()`
  - Provides detailed classification metrics
  - Outputs confusion matrix visualization

### Fixed
- Fixed data loader bug in `data_loader.py:123`
  - Issue: Incorrect handling of missing timestamps
  - Solution: Added explicit timestamp validation and interpolation

### Files Affected
- `src/train.py` (lines 45-67, 89-92)
- `src/evaluate.py` (new function added)
- `src/data_loader.py` (line 123)
- `docs/TRAINING_PROCESS.md` (updated early stopping section)

Examples

Example 1: Creating ML Training Script

Input: "Create a script to train a neural network for image classification"

Steps:

1. Create structured directory layout with src/, data/, models/, docs/
2. Write src/train.py with comprehensive docstrings and inline comments
3. Create README.md with project overview and directory structure
4. Create docs/TRAINING_PROCESS.md describing training methodology
5. Create docs/MODEL_ARCHITECTURE.md explaining model design
6. Create requirements.txt with all dependencies
7. Add data dictionary if custom dataset is used

Expected output: Complete project structure with all documentation files, heavily commented code, and clear organization.

Example 2: Creating Data Cleaning Script

Input: "Write a script to clean customer transaction data"

Steps:

1. Create structured directory with src/, data/raw/, data/processed/, docs/
2. Write src/clean.py with detailed comments explaining each cleaning step
3. Create data/DATA_DICTIONARY.md describing all fields before and after cleaning
4. Create docs/CLEANING_PROCESS.md with step-by-step cleaning methodology
5. Create data/QUALITY_REPORT.md with data quality metrics (completeness, validity)
6. Create README.md with usage instructions and directory guide
7. Add requirements.txt

Expected output: Structured project with comprehensive documentation of data transformations and quality metrics.

Example 3: Modifying Existing Structured Project

Input: "Update the training script to add learning rate scheduling"

Steps:

1. Read existing project structure and understand organization
2. Read src/train.py to understand current implementation
3. Make targeted modifications to training loop
4. Add detailed comments explaining new scheduling logic
5. Update docs/TRAINING_PROCESS.md with new scheduling section
6. Create detailed CHANGELOG.md entry:
   • What changed (specific line numbers)
   • Why it changed (rationale)
   • How it affects training (expected impact)
7. Update README.md if usage instructions changed

Expected output: Modified code with preserved structure, updated documentation, and comprehensive change log.

References

• references/documentation-standards.md: Detailed documentation requirements
• references/directory-templates.md: Standard directory structures for different project types
• references/comment-guidelines.md: Code commenting best practices
• assets/templates/: Ready-to-use project templates

Maintenance

• Sources: Software engineering best practices, data science project standards, documentation conventions
• Last updated: 2026-01-19
• Known limits: Does not enforce specific coding style (PEP8, etc.) beyond documentation requirements