tensorflow-model-trainer
Build tensorflow model trainer operations. Auto-activating skill for ML Training. Triggers on: tensorflow model trainer, tensorflow model trainer Part of the ML Training skill category. Use when working with tensorflow model trainer functionality. Trigger with phrases like "tensorflow model trainer", "tensorflow trainer", "tensorflow". '
Profile →npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill tensorflow-model-trainer --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.
# Tensorflow Model Trainer ## Overview This skill provides automated assistance for tensorflow model trainer tasks within the ML Training domain. ## When to Use This skill activates automatically when you: - Mention "tensorflow model trainer" in your request - Ask about tensorflow model trainer patterns or best practices - Need help with machine learning training skills covering data preparation,
What does the tensorflow-model-trainer skill do?
Build tensorflow model trainer operations. Auto-activating skill for ML Training. Triggers on: tensorflow model trainer, tensorflow model trainer Part of the ML Training skill category. Use when working with tensorflow model trainer functionality. Trigger with phrases like "tensorflow model trainer", "tensorflow trainer", "tensorflow". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill tensorflow-model-trainer --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.