distill-prompt

Condense verbose developer-facing prompts into concise token-efficient LLM instructions.

1|Updated Jan 31, 2026

Search Tags:#prompt engineering #optimization #llm #token efficiency #prompt distillation

Ready to run

npx skills add https://github.com/charly-vibes/wai --skill distill-prompt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distill-prompt
Source: https://github.com/charly-vibes/wai/tree/main/.agents/skills/distill-prompt
Command: npx skills add https://github.com/charly-vibes/wai --skill distill-prompt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines communication with Large Language Models (LLMs) by reducing verbose, human-oriented prompts into concise, token-efficient instructions.

Core Features & Use Cases

Prompt Optimization: Removes unnecessary human-readable text, metadata, and verbose examples from prompts.
Token Efficiency: Reduces LLM input costs and improves processing speed by focusing on essential instructions.
Use Case: You have a detailed prompt for an LLM that includes background information and examples. Use this Skill to extract only the core commands and constraints for the LLM to act upon.

Quick Start

Use the distill-prompt skill to condense the following developer-facing prompt into an LLM-facing prompt.

Frequently Asked Questions about distill-prompt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I condense verbose LLM prompts for token efficiency?▼

You can condense LLM prompts for token efficiency by distilling verbose developer-facing text to remove extraneous human-readable content, metadata, and examples. This retains only essential instructions and rules for execution.

What is prompt distillation in LLM interactions?▼

Prompt distillation is the process of streamlining communication with Large Language Models by reducing verbose, human-oriented prompts into concise, token-efficient instructions. It extracts only the core commands and constraints for the LLM to act upon.

How do I reduce LLM input costs and improve processing speed?▼

To reduce LLM input costs and improve processing speed, optimize your prompts by removing unnecessary human-readable text and verbose examples. Focusing on essential instructions lowers token usage, which directly decreases expenses and accelerates processing.

Can I optimize a detailed prompt with background information for an LLM?▼

Yes, you can optimize a detailed prompt containing background information by extracting only the core commands and constraints. This strips away unnecessary human-readable metadata, leaving structured commands tailored for LLM execution.

Does prompt distillation remove necessary examples from LLM instructions?▼

Prompt distillation removes verbose examples and background information from developer-facing prompts to achieve token efficiency. It focuses solely on retaining essential instructions, rules, and structured commands required for LLM execution.