Dataset Loader

A content creation and management toolkit for drafting, editing, optimizing, and scheduling content from the command line. All operations are logged with timestamps and stored locally.

Commands

Content Operations

Each content command works in two modes: run without arguments to view recent entries, or pass input to record a new entry.

Utility Commands

Data Storage

All data is stored locally at ~/.local/share/dataset-loader/:

• Each content command writes to its own log file (e.g., draft.log, edit.log, hashtags.log)
• Entries are stored as timestamp|value pairs (pipe-delimited)
• All actions are tracked in history.log with timestamps
• Export generates files in the data directory (export.json, export.csv, or export.txt)

Requirements

• Bash (with set -euo pipefail)
• Standard Unix utilities: date, wc, du, grep, tail, cat, sed
• No external dependencies or API keys required

When to Use

• To log and track content creation workflows (draft, edit, optimize, schedule)
• To maintain a searchable history of content operations
• To manage hashtags, hooks, CTAs, and headlines in a structured way
• To export accumulated content records in JSON, CSV, or plain text format
• As part of larger content automation pipelines

Examples

# Draft new content
dataset-loader draft "Blog post about AI trends in 2026"

# View recent edits
dataset-loader edit

# Record hashtags
dataset-loader hashtags "#AI #MachineLearning #DataScience"

# Create a headline
dataset-loader headline "5 Ways AI Is Changing Data Science"

# Search across all logs
dataset-loader search "AI"

# Export everything as CSV
dataset-loader export csv

# Check statistics
dataset-loader stats

# View recent activity
dataset-loader recent

# Health check
dataset-loader status

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