What are the common prompt anti-patterns this skill avoids?

It helps eliminate verbose explanations, historical context dumping, overlapping tool definitions, and vague language like 'might' or 'should'.

What is context engineering?

Context engineering is the practice of curating the most relevant, high-signal tokens for an LLM to process, ensuring the model stays focused on the task without being overwhelmed by noise.

Is this skill based on official Anthropic guidelines?

Yes, the principles and philosophies in this skill are directly based on Anthropic's 'Effective Context Engineering for AI Agents' standards.

How does this skill help with token limits?

It implements 'Progressive Discovery' and 'Just-in-Time' loading, which uses lightweight identifiers instead of full data dumps to preserve the LLM's finite attention budget.

AI Prompting Standards

Name: AI Prompting Standards
Author: rafaelcalleja

byrafaelcalleja

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数据科学与机器学习

Optimizes AI agent performance through Anthropic-based context engineering and prompt structure standards.

关于

The AI Prompting Standards skill provides a framework for advanced prompt and context engineering based on Anthropic best practices. It helps developers move beyond basic instructions by focusing on the 'attention budget' of Large Language Models, emphasizing high-signal tokens and efficient context management. By implementing progressive discovery and structured Markdown patterns, this skill ensures that AI agents remain accurate and performant even as project complexity grows, making it an essential tool for anyone building sophisticated agentic workflows.

主要功能

Comprehensive checklist for validating instructions and constraints
Signal-to-noise optimization to reduce LLM hallucination and verbosity
1 GitHub stars
Anthropic-aligned prompt structure and markdown standards
Progressive discovery patterns for just-in-time information loading
Context engineering for optimal token usage and attention management

使用场景

Refining long-context instructions to improve tool-calling accuracy
Designing complex system prompts for specialized AI agents
Standardizing documentation and prompt headers across a development team

关于

主要功能

Comprehensive checklist for validating instructions and constraints
Signal-to-noise optimization to reduce LLM hallucination and verbosity
1 GitHub stars
Anthropic-aligned prompt structure and markdown standards
Progressive discovery patterns for just-in-time information loading
Context engineering for optimal token usage and attention management

使用场景

Refining long-context instructions to improve tool-calling accuracy
Designing complex system prompts for specialized AI agents
Standardizing documentation and prompt headers across a development team