What are the emphasis levels in prompt-creation?

The skill uses a 6-level syntax ranging from Level 1 (suggestions like 'Please do') to Level 6 (absolute rules like 'CRITICAL - ZERO TOLERANCE') to signal the importance of specific constraints.

How does 'Precognition' improve prompt performance?

Precognition uses tags to force the model to reason through its logic and analyze key points before generating a final answer, which significantly reduces hallucinations.

What is the 9-element prompt structure used by this skill?

It is a comprehensive framework covering Task Context, Tone, Rules, Examples (Few-shot), Input Data, Immediate Task, Precognition (Scratchpad), Output Formatting, and Prefilling.

Can I use these templates for models other than Claude?

While specifically optimized for Anthropic's models and conventions, the structured logic, XML tagging, and few-shot patterns are highly effective across most modern LLMs.

Claude Prompt Engineer

Name: Claude Prompt Engineer
Author: fusengine

byfusengine

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Data Science & ML

Generates high-performance, structured prompts using official Anthropic conventions and 2025 best practices.

About

This skill equips Claude Code with a standardized framework for architecting sophisticated prompts based on Anthropic’s official 9-element structure. It provides an engineering-first approach to prompt design, incorporating advanced techniques like Chain-of-Thought (precognition) via scratchpad tags, few-shot examples, and a 6-level hierarchical emphasis system. By following a rigorous creation workflow, it helps developers build robust system instructions that minimize ambiguity, prevent hallucinations, and optimize cognitive performance for complex agent systems.

Key Features

Implements the official Anthropic 9-element prompt structure
Automates prompt refinement from objective definition to edge-case testing
Uses 'Precognition' scratchpad tags for enhanced logical reasoning
1 GitHub stars
Applies a 6-level hierarchy for rule enforcement and constraint weighting
Standardizes few-shot examples using XML-tagging conventions

Use Cases

Architecting complex system prompts for autonomous AI agents
Converting informal developer instructions into structured production-grade prompts
Refining existing LLM instructions to improve reliability and output formatting

About

Key Features

Implements the official Anthropic 9-element prompt structure
Automates prompt refinement from objective definition to edge-case testing
Uses 'Precognition' scratchpad tags for enhanced logical reasoning
1 GitHub stars
Applies a 6-level hierarchy for rule enforcement and constraint weighting
Standardizes few-shot examples using XML-tagging conventions

Use Cases

Architecting complex system prompts for autonomous AI agents
Converting informal developer instructions into structured production-grade prompts
Refining existing LLM instructions to improve reliability and output formatting