data-backup

name	data-backup
description	Smart automated backup system with skill integration. Detects project type (notebooks, data files, HackMD docs) and applies appropriate cleanup before backup. Rolling daily backups, compressed milestones, and CHANGELOG tracking.
version	2.0.0

Smart Backup System with Skill Integration

Supporting files in this directory:

• MANIFEST_BACKUPS.md -- MANIFEST-aware intelligent backups
• FULL_PROJECT_BACKUPS.md -- Full project backups, selective inclusion/exclusion, path verification
• ADVANCED_USAGE.md -- Custom scripts, multiple file backups, real-world examples

When to Use This Skill

Use this skill when:

• Working on any project with files that change over time
• Jupyter notebooks, data files (CSV/TSV), HackMD presentations, or mixed projects
• Need intelligent cleanup before backup (clear outputs, remove debug code)
• Want to track what changed when (data provenance)
• Need professional backup workflow for collaboration or publication
• Want context-aware backups that use other skills intelligently

The Problem

Long-running data enrichment projects risk:

• Losing days of work from accidental overwrites
• Unable to revert to previous data states
• No documentation of what changed when
• Running out of disk space from manual backups
• Confusion about which version is current

Solution: Smart Two-Tier Backup System with Skill Integration

Core Features

1. Intelligent Detection - Automatically detects project type and files to backup
2. Skill Integration - Uses jupyter-notebook, hackmd, and other skills for pre-backup cleanup
3. Daily backups - Rolling 7-day window (auto-cleanup)
4. Milestone backups - Permanent, compressed (gzip ~80% reduction)
5. CHANGELOG - Automatic documentation of all changes
6. Session Integration - Prompts for backup when exiting Claude Code session

Smart Detection & Integration

The backup system automatically detects your project type and applies appropriate cleanup:

Jupyter Notebooks (uses jupyter-notebook skill):

• Detects: *.ipynb files
• Pre-backup cleanup: Clear all cell outputs, remove cells tagged 'debug' or 'remove', validate notebooks

HackMD/Presentations (uses hackmd skill):

• Detects: *.md files with slideOptions: frontmatter
• Pre-backup cleanup: Validate SVG elements, check slide separators, verify YAML frontmatter

Data Files (native handling):

• Detects: *.csv, *.tsv, *.xlsx files
• Pre-backup cleanup: Validate file integrity, check for corruption

Python Projects (uses managing-environments skill):

• Detects: requirements.txt, environment.yml, venv/, .venv/
• Pre-backup cleanup: Remove .pyc, __pycache__, .pytest_cache, clean build artifacts

Mixed Projects: Detects all of the above and applies appropriate cleanup for each file type.

Directory Structure

For data-only projects:

project/
├── your_data_file.csv          # Main working file
├── backup_project.sh           # Smart backup script
└── backups/
    ├── daily/                  # Rolling 7-day backups
    ├── milestones/             # Permanent compressed backups
    ├── CHANGELOG.md            # Auto-generated change log
    └── README.md               # User documentation

For mixed projects (notebooks + data):

project/
├── analysis.ipynb              # Jupyter notebooks
├── data.csv                    # Data files
├── backup_project.sh           # Smart backup script
└── backups/
    ├── daily/                  # Rolling 7-day backups
    │   └── backup_2026-01-17/
    │       ├── notebooks/      # Cleaned (no outputs)
    │       └── data/
    ├── milestones/             # Permanent compressed backups
    ├── CHANGELOG.md
    └── README.md

Storage Efficiency

• Daily backups: ~5.4 MB (7 days x 770KB)
• Milestone backups: ~200KB each compressed (80% size reduction with gzip)
• Total: <10 MB for complete project history
• Auto-cleanup: Old daily backups delete after 7 days

Implementation

Quick Start with /backup Command

First time - Setup the backup system:

/backup

This will:

• Detect your project type (notebooks, data files, presentations, etc.)
• Set up appropriate backup scripts with smart cleanup
• Create backup directory structure
• Optionally configure automated backups

Daily usage - Create backups:

/backup                    # Daily backup with smart cleanup
/backup milestone "desc"   # Milestone backup
/backup list              # View all backups
/backup restore DATE      # Restore from backup

What Happens During Backup

Smart cleanup before backup:

1. Detects file types in your project
2. Applies skill-specific cleanup:
   • Notebooks: Clear outputs, remove debug cells
   • HackMD: Validate SVG, check formatting
   • Python: Remove .pyc, __pycache__
   • Data: Validate integrity
3. Creates organized backup with cleaned files
4. Updates CHANGELOG with what was backed up

Manual Script Usage (Alternative)

./backup_project.sh                           # Daily backup
./backup_project.sh milestone "description"   # Milestone
./backup_project.sh list                      # List backups
./backup_project.sh restore 2026-01-23        # Restore

When to Create Milestones

• After adding new data sources (GenomeScope, karyotypes, external APIs)
• Before major data transformations or filtering
• When completing analysis sections
• Before submitting/publishing
• Before sharing with collaborators
• After recovering missing data

Key Features

Safety Features

1. Never overwrites without asking - Prompts before overwriting existing backups
2. Safety backup before restore - Creates backup of current state before any restore
3. Automatic cleanup - Old daily backups auto-delete (configurable)
4. Complete audit trail - CHANGELOG tracks everything
5. Milestone protection - Important versions preserved forever (compressed)

CHANGELOG Tracking

The CHANGELOG.md automatically documents:

• Date of each backup
• Type (daily vs milestone)
• Description of changes (for milestones)
• Major modifications made to data

Example CHANGELOG:

## 2026-01-23
- **MILESTONE**: Recovered VGP accessions (backup created)
  - Added columns: `accession_recovered`, `accession_recovered_all`
  - Recovered 5 VGP accessions from NCBI
- Daily backup created at 2026-01-23 15:00:00

## 2026-01-22
- Enriched GenomeScope data for 21 species from AWS repository
- Added column: `genomescope_path` with direct links to summary files

Using /backup Command

Setup mode (first run): /backup -- Detects project type, sets up scripts, creates directory structure.

Daily backup mode: /backup -- Quick daily backup.

Milestone mode: /backup milestone "description of changes" -- e.g., /backup milestone "added heterozygosity data"

List and restore:

/backup list              # Show all available backups
/backup restore 2026-01-23 # Restore from specific date

Configuration: Edit backup_project.sh to change retention days (default: 7), backup directory location, or custom cleanup rules.

Benefits for Data Analysis

• Data Provenance: CHANGELOG documents every modification; clear audit trail for methods sections in papers
• Confidence to Experiment: Easy rollback encourages trying different approaches safely
• Professional Workflow: Matches publication standards; reviewers can verify data processing steps
• Collaboration-Ready: Team members can understand data history and enrichment process

Session Integration with /safe-exit

When you end a Claude Code session with /safe-exit, the system automatically:

1. Detects if backup system exists in the current project
2. Prompts for backup if system is configured (daily, milestone, skip, or cancel)
3. Performs cleanup and backup if requested
4. Prompts for Obsidian session summary (if obsidian skill is available)
5. Exits session cleanly

This ensures you never forget to backup AND document your work at the end of your session!

Example Workflow

Monday Morning

/backup                          # Daily backup with smart cleanup
# Work on notebooks and data enrichment all day
/backup milestone "added karyotype data for 50 new species"

End of session

/safe-exit
# Prompted: daily backup -> backup complete -> session summary -> exit

Friday (oops, made a mistake!)

/backup list                     # Check available backups
/backup restore 2026-01-23       # Restore from Wednesday

MANIFEST-Aware Backups

For projects with MANIFEST files, use intelligent backups that include only essential files. See MANIFEST_BACKUPS.md for the full pattern, script templates, inclusion/exclusion rules, and integration with the /backup command.

Full Project Backups

For projects where both code and data change, selective full-project backups capture the complete state without bloat. See FULL_PROJECT_BACKUPS.md for implementation patterns, backup strategy comparison, size benchmarks, and path verification guidance.

Advanced Usage

For custom backup script templates, handling multiple files, viewing compressed milestones, and real-world examples, see ADVANCED_USAGE.md.

Best Practices

1. Create daily backups at session start - Make it a habit
2. Milestone after every major change - Don't rely on memory
3. Use descriptive milestone names - "added genomescope" not "updates"
4. Check CHANGELOG before sharing - Verify data provenance is clear
5. List backups periodically - Ensure auto-cleanup is working
6. Test restore once - Verify you know how to recover

Troubleshooting

Backup script not found

ls -l backup_project.sh   # Check if backup system is set up
/backup                    # Set up if needed

Disk space running low

du -sh backups/            # Check backup sizes
# Reduce retention: edit DAYS_TO_KEEP=3 in backup_table.sh
# Manually clean old milestones if needed

CHANGELOG getting too large

tail -100 backups/CHANGELOG.md > backups/CHANGELOG_recent.md
mv backups/CHANGELOG.md backups/CHANGELOG_archive.md
mv backups/CHANGELOG_recent.md backups/CHANGELOG.md

Summary

• Two-tier system: Daily rolling + permanent milestones
• Storage efficient: Gzip compression (~80% reduction)
• Auto-cleanup: 7-day rolling window for dailies
• Complete audit trail: CHANGELOG tracks all changes
• Safety first: Never overwrites without confirmation
• Global installer: Use across all projects
• Professional workflow: Publication-ready data provenance