optimizing-prompts_skill

Overview

This skill optimizes prompts for large language models to reduce token usage, lower costs, and improve runtime performance. It streamlines wording and removes redundancy while preserving or improving the instruction clarity and expected output. Use it to make prompts more efficient for production or experimentation.

How this skill works

The skill analyzes the supplied prompt to detect verbosity, duplicate instructions, and ambiguous phrasing. It rewrites the prompt using concise language, targeted directives, and prioritized constraints to reduce token count. It returns the optimized prompt plus an explanation of changes, token savings estimates, and suggested variants for different trade-offs (brevity vs. explicitness).

When to use it

• You want to cut API costs by reducing tokens per call.
• Responses are slow and you need faster turnaround without losing quality.
• The model output is inconsistent and prompt clarity may help.
• Preparing prompts for high-throughput or real-time systems.
• Before scaling prompts across multiple LLMs or environments.

Best practices

• Provide the full original prompt and any required context for accurate optimization.
• State your primary objective (cost, speed, accuracy) so rewrites match priorities.
• Keep key constraints explicit; don’t remove essential requirements when shortening.
• Test optimized prompts with real inputs and iterate based on model behavior.
• Use suggested variants when balancing brevity and specificity for different models.

Example use cases

• Convert a long product brief into a compact instruction that preserves output structure and details.
• Trim a multi-step data-extraction prompt to reduce tokens while keeping parsing reliability.
• Produce a short high-priority prompt for low-latency inference in an interactive application.
• Generate both a concise and a verbose variant so you can A/B test cost vs. fidelity.
• Optimize prompts for deployment across different LLM providers with varied token pricing.