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
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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.
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). |
All data is stored in ~/.local/share/machine-learning-roadmap/:
.log file (e.g., draft.log, headline.log, translate.log)YYYY-MM-DD HH:MM|<value> formathistory.log tracks all actions across command typesexport.json, export.csv, or export.txtset -euo pipefaildate, wc, du, tail, grep, sed, cat)draft and outline to capture ideas and structure articles, blog posts, or course materials about machine learning topicsheadline and hooks entries to brainstorm attention-grabbing titles and opening lines for ML contentoptimize, rewrite, and tone to track iterations as you refine ML tutorials, documentation, or marketing copytranslate entries when adapting ML learning materials for different language audiencesschedule and cta to plan publication timelines and track call-to-action variations for ML courses or newsletters# 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
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:
machine-learning-roadmap stats > report.txt
Set the DATA_DIR inside the script or modify the default path ~/.local/share/machine-learning-roadmap/ to change where data is stored.
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這個 Skill 本質上是一個內容創作日誌記錄器,質量中等偏下。雖然指令碼可正常執行,但存在明顯的命名與功能不符問題——名為"機器學習路線圖",實際只是記錄標題、大綱等內容的工具,沒有學習路徑規劃或資源推薦功能。此外還存在版本號混亂、文件描述與實際技術棧不一致的問題。功能單一,實用性有限,不推薦作為主要工具使用。