Skill: ckchzh/ml-roadmap (version 2.0.0)

--- version: "2.0.0" name: Machine Learning Roadmap description: "A roadmap connecting many of the most important concepts in machine learning, how to learn them and machine learning roadmap, python, data, data-science." --- # 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 1. **Planning your ML learning path** — use `outline` and `draft` to structure a study roadmap covering supervised learning, deep learning, NLP, computer vision, and more 2. **Creating ML educational content** — use `headline`, `hooks`, `cta`, and `hashtags` to craft engaging posts or articles about machine learning concepts 3. **Scheduling study sessions** — use `schedule` to log when you plan to study specific ML topics and track your progress over time 4. **Refining technical writing** — use `rewrite`, `tone`, and `optimize` to polish ML blog posts, documentation, or course materials 5. **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: ```bash ml-roadmap stats > roadmap-report.txt ml-roadmap export csv ``` --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


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