data-normalization-tool
Process data normalization tool operations. Auto-activating skill for ML Training. Triggers on: data normalization tool, data normalization tool Part of the ML Training skill category. Use when working with data normalization tool functionality. Trigger with phrases like "data normalization tool", "data tool", "data". '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill data-normalization-tool --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Normalization Tool ## Overview This skill provides automated assistance for data normalization tool tasks within the ML Training domain. ## When to Use This skill activates automatically when you: - Mention "data normalization tool" in your request - Ask about data normalization tool patterns or best practices - Need help with machine learning training skills covering data preparation, mode
What does the data-normalization-tool skill do?
Process data normalization tool operations. Auto-activating skill for ML Training. Triggers on: data normalization tool, data normalization tool Part of the ML Training skill category. Use when working with data normalization tool functionality. Trigger with phrases like "data normalization tool", "data tool", "data". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill data-normalization-tool --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.