optimizing-prompts
Execute this skill optimizes prompts for large language models (llms) to reduce token usage, lower costs, and improve performance. it analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more conci... Use when optimizing performance. Trigger with phrases like ''optimize'', ''performance'', or ''speed up''. '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill optimizing-prompts --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.
# Ai Ml Engineering Pack Optimize LLM prompts for reduced token usage, lower costs, and improved output quality by identifying redundancies, simplifying instructions, and restructuring for clarity. ## Overview Refine prompts for optimal LLM performance. It streamlines prompts to minimize token count, thereby reducing costs and enhancing response speed, all while maintaining or improving output qua
What does the optimizing-prompts skill do?
Execute this skill optimizes prompts for large language models (llms) to reduce token usage, lower costs, and improve performance. it analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more conci... Use when optimizing performance. Trigger with phrases like ''optimize'', ''performance'', or ''speed up''. '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill optimizing-prompts --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.