Ml Roadmap

A roadmap connecting many of the most important concepts in machine learning, how to learn them and machine learning roadmap, python, data, data-science.

BytesAgain2@ckchzh

Install

openclaw skills install @ckchzh/ml-roadmap

Machine Learning Roadmap

A thorough content toolkit for planning and tracking your machine learning learning journey. Draft study plans, organize topics, create outlines, schedule learning sessions, and manage your ML education roadmap — all from the command line.

Commands

Command	Description
`ml-roadmap draft <input>`	Draft a new ML learning plan or content entry
`ml-roadmap edit <input>`	Edit an existing entry or refine content
`ml-roadmap optimize <input>`	Optimize content for clarity or effectiveness
`ml-roadmap schedule <input>`	Schedule learning sessions or content publication
`ml-roadmap hashtags <input>`	Generate relevant hashtags for ML topics
`ml-roadmap hooks <input>`	Create engaging hooks for ML content
`ml-roadmap cta <input>`	Generate call-to-action text for ML resources
`ml-roadmap rewrite <input>`	Rewrite content with improved structure
`ml-roadmap translate <input>`	Translate ML content between languages
`ml-roadmap tone <input>`	Adjust the tone of ML content (formal, casual, etc.)
`ml-roadmap headline <input>`	Generate compelling headlines for ML topics
`ml-roadmap outline <input>`	Create structured outlines for ML subjects
`ml-roadmap stats`	Show summary statistics across all entry types
`ml-roadmap export <fmt>`	Export all data (formats: `json`, `csv`, `txt`)
`ml-roadmap search <term>`	Search across all entries by keyword
`ml-roadmap recent`	Show the 20 most recent activity log entries
`ml-roadmap status`	Health check — version, disk usage, last activity
`ml-roadmap help`	Show the built-in help message
`ml-roadmap version`	Print the current version (v2.0.0)

Each content command (draft, edit, optimize, etc.) works in two modes:

Without arguments — displays the 20 most recent entries of that type
With arguments — saves the input as a new timestamped entry

Data Storage

All data is stored as plain-text log files in ~/.local/share/ml-roadmap/:

Each command type gets its own log file (e.g., draft.log, edit.log, outline.log)
Entries are stored in timestamp|value format for easy parsing
A unified history.log tracks all activity across command types
Export to JSON, CSV, or TXT at any time with the export command

Set the ML_ROADMAP_DIR environment variable to override the default data directory.

Requirements

Bash 4.0+ (uses set -euo pipefail)
Standard Unix utilities: date, wc, du, tail, grep, sed, cat
No external dependencies or API keys required

When to Use

Planning your ML learning path — use outline and draft to structure a study roadmap covering supervised learning, deep learning, NLP, computer vision, and more
Creating ML educational content — use headline, hooks, cta, and hashtags to craft engaging posts or articles about machine learning concepts
Scheduling study sessions — use schedule to log when you plan to study specific ML topics and track your progress over time
Refining technical writing — use rewrite, tone, and optimize to polish ML blog posts, documentation, or course materials
Tracking content creation history — use stats, search, and recent to review what you've written, find past entries, and measure productivity

Examples

bash

# Draft a new learning plan for deep learning fundamentals
ml-roadmap draft "Week 1: Neural network basics — perceptrons, activation functions, backprop"

# Create an outline for a blog post on model selection
ml-roadmap outline "Comparing Random Forest vs XGBoost: when to use each, key hyperparameters, pros/cons"

# Generate a headline for an ML tutorial
ml-roadmap headline "Beginner-friendly guide to building your first image classifier with PyTorch"

# Schedule a study session
ml-roadmap schedule "Saturday 10am: Work through Stanford CS229 Lecture 5 — Support Vector Machines"

# Export all your entries to JSON for backup
ml-roadmap export json

Output

All commands print results to stdout. Redirect to a file if needed: