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