How can this skill help reduce my API costs?

The skill includes specific guidance on token efficiency and performance optimization, helping you remove redundant phrases and consolidate instructions to minimize token usage.

Does it support complex reasoning tasks?

Yes, it implements Chain-of-Thought (CoT) prompting techniques which are designed to improve analytical and mathematical reasoning accuracy by 30-50%.

Can I use this for building autonomous AI agents?

Absolutely. It follows Anthropic's official best practices for agent prompting, including managing context windows and setting appropriate degrees of freedom for agent behavior.

Is there support for prompt versioning and testing?

Yes, the skill encourages systematic improvement through A/B testing, performance measurement (accuracy and consistency), and treating prompts as version-controlled code.

What is the Prompt Engineering skill for Claude Code?

It is a specialized skill that provides structured patterns, such as Chain-of-Thought and few-shot learning, to help developers write more effective instructions for LLMs.

Prompt Engineering

Name: Prompt Engineering
Author: allohamora

byallohamora

データサイエンスとML

Implements advanced LLM interaction patterns and optimization techniques to maximize Claude's performance and reliability.

概要

This skill provides a comprehensive framework for designing, testing, and optimizing interactions with Large Language Models. It equips developers with advanced techniques such as few-shot learning, chain-of-thought prompting, and reusable template systems to ensure consistent, high-quality AI outputs. By formalizing instruction hierarchies and error recovery strategies, it enables the creation of robust AI agents and complex automation workflows while emphasizing token efficiency and latency reduction. Whether you are building sub-agents or optimizing production prompt templates, this skill offers the domain-specific guidance needed to master model controllability.

主な機能

Instruction hierarchy and error recovery strategies
Systematic prompt optimization and A/B testing
Few-shot and Chain-of-Thought reasoning patterns
Token efficiency and latency optimization techniques
0 GitHub stars
Reusable modular template systems with variables

ユースケース

Standardizing LLM interaction patterns across development teams
Developing high-reliability AI agents for production
Optimizing complex multi-step reasoning and analytical tasks

概要

主な機能

Instruction hierarchy and error recovery strategies
Systematic prompt optimization and A/B testing
Few-shot and Chain-of-Thought reasoning patterns
Token efficiency and latency optimization techniques
0 GitHub stars
Reusable modular template systems with variables

ユースケース

Standardizing LLM interaction patterns across development teams
Developing high-reliability AI agents for production
Optimizing complex multi-step reasoning and analytical tasks