Can this skill help reduce my API costs?

Yes, it specifically prioritizes token efficiency by cutting redundancies and optimizing prompt structure, which can lead to lower token usage in production environments.

How does this skill handle vague instructions?

It uses an activation protocol that detects missing context (like format, audience, or location) and asks clarifying questions before performing the requested task.

What is the primary goal of the Prompt Engineer skill?

The primary goal is to build, analyze, and optimize LLM prompts and technical documentation, focusing on clarity, structure, and the removal of unnecessary 'fluff'.

Does it support different Claude models?

Yes, it includes specific guidance for tailoring prompts to different models, including Haiku for simple tasks, Sonnet for balance, and Opus for complex reasoning.

Prompt Engineer

Name: Prompt Engineer
Author: Shavakan

byShavakan

•

データサイエンスとML

Builds, optimizes, and debugs LLM prompts and technical documentation using advanced prompt engineering patterns and brutal concision.

概要

The Prompt Engineer skill transforms Claude into a specialized consultant for LLM orchestration and technical writing. It proactively identifies ambiguous requests, enforcing a strict clarification protocol before drafting content to ensure maximum accuracy. By applying rigorous principles such as 'hostile interpreter' validation, XML delimiting, and token efficiency optimization, this skill helps developers create robust system prompts and high-quality technical documentation that is free of conversational fluff and redundant phrasing.

主な機能

Generation of concise technical specs, READMEs, and implementation guides
Validation against 'hostile interpreter' scenarios to prevent prompt drift
Implementation of advanced patterns like Chain-of-Thought and Few-Shot prompting
Optimization of prompts for token efficiency and logical consistency
2 GitHub stars
Proactive clarification of ambiguous writing and documentation requirements

ユースケース

Standardizing project documentation with a focus on brutal concision and clarity
Refining raw project requirements into production-ready system instructions
Debugging existing prompts that are failing or producing inconsistent results

概要

主な機能

Generation of concise technical specs, READMEs, and implementation guides
Validation against 'hostile interpreter' scenarios to prevent prompt drift
Implementation of advanced patterns like Chain-of-Thought and Few-Shot prompting
Optimization of prompts for token efficiency and logical consistency
2 GitHub stars
Proactive clarification of ambiguous writing and documentation requirements

ユースケース

Standardizing project documentation with a focus on brutal concision and clarity
Refining raw project requirements into production-ready system instructions
Debugging existing prompts that are failing or producing inconsistent results