structured-content-storage

Files (10)

• SKILL.md
• CHANGELOG-template.md · assets/templates/
• DATA_DICTIONARY-template.md · assets/templates/
• PROCESS-template.md · assets/templates/
• data-processing-README.md · assets/templates/
• ml-project-README.md · assets/templates/
• comment-guidelines.md · references/
• directory-templates.md · references/
• documentation-standards.md · references/
• index.md · references/

SKILL.md (first lines)

# 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/

From foryourhealth111-pixel/Vibe-Skills, Apache-2.0.

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