Condense verbose developer-facing prompts into concise token-efficient LLM instructions.
npx skills add https://github.com/charly-vibes/wai --skill distill-prompt
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
This Skill streamlines communication with Large Language Models (LLMs) by reducing verbose, human-oriented prompts into concise, token-efficient instructions.
Use the distill-prompt skill to condense the following developer-facing prompt into an LLM-facing prompt.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
thejefflarsonDetect and fix prompt construction patterns that allow user input to influence system prompts.
zhuangbiaoweiAccelerate LLM inference with speculative decoding, Medusa, and lookahead decoding.
Blake-JohnWrite, refactor, and evaluate LLM prompts with JSON schemas.
thimsluggaConvert verbose technical prompts into condensed caveman-style responses.
