Machine Learning Roadmap

Follow a structured ML roadmap connecting concepts, tools, and learning resources. Use when planning study paths, discovering resources, mapping skills.

Security Scan

This skill is internally consistent: it is a local content-logging/authoring toolkit that stores user entries under ~/.local/share/machine-learning-roadmap with no external network calls or credential access, but it will persist any input unencrypted on disk.

Purpose & Capability

The name/description (ML content roadmap and content toolkit) matches the included bash script and SKILL.md. The script implements commands for drafting, editing, exporting, searching, and stats — all relevant to a content-management roadmap tool. No unrelated credentials, binaries, or installs are requested.

Instruction Scope

Runtime instructions and the script operate only on files under the user data directory (~/.local/share/machine-learning-roadmap). The script logs every user entry and a history file. It does not make network requests or access other system config. Note: user-provided text is stored unencrypted on disk and included verbatim in exports; export code does not escape values (may produce malformed JSON) which can cause accidental leakage if exported files are shared.

Install Mechanism

No install spec is provided (instruction-only with an included script). No downloads or package installs are performed. The script is plain bash and uses only standard Unix utilities.

Credentials

The skill requests no environment variables, credentials, or config paths. The single data directory it uses is local to the user home and matches the described functionality.

Persistence & Privilege

always is false and the skill does not modify other skills or system-wide settings. It persists its own data under the user's home directory only.

Assessment

This skill appears to do what it says and only writes local files, but be aware it will persist any text you enter (including secrets) in cleartext under ~/.local/share/machine-learning-roadmap. If you install it: (1) avoid entering passwords, API keys, or other sensitive data into commands; (2) consider changing DATA_DIR to an encrypted location or restrict filesystem permissions; (3) if you plan to share exports, inspect them first — export code does not escape values and may produce malformed or unsafe output; (4) review the included script before running to confirm you accept its local storage behavior and the BytesAgain attribution if provenance matters.

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

# 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:

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.

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