Skill: ckchzh/machine-learning-roadmap (version 1.0.0)

--- name: Machine Learning Roadmap description: "Follow a structured ML roadmap connecting concepts, tools, and learning resources. Use when planning study paths, discovering resources, mapping skills." version: "1.0.0" license: MIT runtime: python3 --- # Machine Learning Roadmap Machine Learning Roadmap v2.0.0 — a content toolkit for drafting, editing, optimizing, and managing machine learning content. Create outlines, write headlines, generate CTAs, manage hashtags, rewrite content, translate text, and adjust tone — all tracked with timestamped entries stored locally. ## Commands Run `scripts/script.sh <command> [args]` to use. | Command | Description | |---------|-------------| | `draft <input>` | Record a draft entry. Without args, shows the 20 most recent draft entries. | | `edit <input>` | Record an edit entry. Without args, shows recent edit entries. | | `optimize <input>` | Record an optimization entry. Without args, shows recent optimize entries. | | `schedule <input>` | Record a scheduling entry. Without args, shows recent schedule entries. | | `hashtags <input>` | Record a hashtags entry. Without args, shows recent hashtags entries. | | `hooks <input>` | Record a hooks entry. Without args, shows recent hooks entries. | | `cta <input>` | Record a call-to-action entry. Without args, shows recent CTA entries. | | `rewrite <input>` | Record a rewrite entry. Without args, shows recent rewrite entries. | | `translate <input>` | Record a translation entry. Without args, shows recent translate entries. | | `tone <input>` | Record a tone adjustment entry. Without args, shows recent tone entries. | | `headline <input>` | Record a headline entry. Without args, shows recent headline entries. | | `outline <input>` | Record an outline entry. Without args, shows recent outline entries. | | `stats` | Show summary statistics across all entry types (counts, data size). | | `export <fmt>` | Export all data in `json`, `csv`, or `txt` format. | | `search <term>` | Search all log files for a term (case-insensitive). | | `recent` | Show the 20 most recent entries from the activity history. | | `status` | Health check — version, data directory, entry count, disk usage. | | `help` | Show help message with all available commands. | | `version` | Show version string (`machine-learning-roadmap v2.0.0`). | ## Data Storage All data is stored in `~/.local/share/machine-learning-roadmap/`: - Each command type writes to its own `.log` file (e.g., `draft.log`, `headline.log`, `translate.log`) - Entries are timestamped in `YYYY-MM-DD HH:MM|<value>` format - A unified `history.log` tracks all actions across command types - Export files are written to the same directory as `export.json`, `export.csv`, or `export.txt` ## Requirements - Bash 4+ with `set -euo pipefail` - Standard Unix utilities (`date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`) - No external dependencies — works out of the box on Linux and macOS ## When to Use 1. **Drafting ML content** — use `draft` and `outline` to capture ideas and structure articles, blog posts, or course materials about machine learning topics 2. **Headline and hook creation** — record `headline` and `hooks` entries to brainstorm attention-grabbing titles and opening lines for ML content 3. **Content optimization** — use `optimize`, `rewrite`, and `tone` to track iterations as you refine ML tutorials, documentation, or marketing copy 4. **Multi-language content** — record `translate` entries when adapting ML learning materials for different language audiences 5. **Content scheduling and CTAs** — use `schedule` and `cta` to plan publication timelines and track call-to-action variations for ML courses or newsletters ## Examples ```bash # Draft a new ML blog post idea machine-learning-roadmap draft "Introduction to Neural Networks: A Beginner's Guide" # Create an outline for a tutorial machine-learning-roadmap outline "1. What is ML? 2. Supervised vs Unsupervised 3. Tools 4. Practice Projects" # Record a headline variation machine-learning-roadmap headline "5 Python Libraries Every ML Engineer Must Know in 2025" # Generate hashtags for social media machine-learning-roadmap hashtags "#MachineLearning #AI #DeepLearning #Python #DataScience" # Export all content data as CSV machine-learning-roadmap export csv # Search for entries mentioning a topic machine-learning-roadmap search "neural" # View summary statistics machine-learning-roadmap stats ``` ## Output All commands print results to stdout. Each recording command confirms the save and shows the total entry count for that category. Redirect output to a file with: ```bash machine-learning-roadmap stats > report.txt ``` ## Configuration Set the `DATA_DIR` inside the script or modify the default path `~/.local/share/machine-learning-roadmap/` to change where data is stored. --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


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