prompting

Name: prompting
Availability: InStock
Author: Hieubkav

Standardize AI agent prompt design with Markdown structure and frontmatter-driven discovery.

Updated Feb 23, 2025

Search Tags:#llm #best-practices #prompting #context-engineering #prompt-engineering #ai-agents

Ready to run

npx skills add https://github.com/Hieubkav/Ph-ng-Kh-m-Ng-c-Nh-n --skill prompting-hieubkav

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompting
Source: https://github.com/Hieubkav/Ph-ng-Kh-m-Ng-c-Nh-n/tree/main/.claude/skills/prompting
Command: npx skills add https://github.com/Hieubkav/Ph-ng-Kh-m-Ng-c-Nh-n --skill prompting-hieubkav

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompts are the primary interface for guiding AI agents; this Skill provides a standardized framework for crafting concise, high-signal prompts and managing the context to improve reliability and outcomes.

Core Features & Use Cases

Standardized prompting guidelines based on Anthropic best practices for clarity, structure, progressive discovery, and signal-to-noise optimization.
Guidance for context engineering, just-in-time loading, and sub-agent architectures to improve efficiency and reliability.
Use Case: Designing prompts for AI agents in complex workflows to minimize confusion and maximize alignment.

Quick Start

Craft a concise, structured prompt that loads detailed information only when needed.

Frequently Asked Questions about prompting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is context engineering for AI agents and why does it matter?▼

Standardized prompt engineering enforces Markdown structure and frontmatter-driven discovery to maximize AI clarity. It provides guidelines for concise, high-signal prompt design based on best practices for progressive information disclosure.

How do I structure prompts for AI agents to minimize confusion?▼

Structure prompts for AI agents using enforced Markdown formatting and frontmatter-driven discovery. This standardized framework ensures high-signal context loading, progressive information discovery, and clear alignment with complex workflow requirements.

What's the best way to manage context loading for LLM workflows?▼

The best way to manage context loading for LLM workflows is just-in-time information retrieval. This approach provides detailed data only when needed, optimizing the signal-to-noise ratio and improving efficiency across sub-agent architectures.

Can I use these prompting guidelines for sub-agent architectures?▼

Yes, you can use these prompting guidelines for sub-agent architectures. The framework provides specific guidance for context engineering and progressive discovery, improving efficiency and reliability when configuring multi-agent AI workflows.

Do I need specific dependencies to implement standardized prompt design?▼

No specific dependencies are required to implement standardized prompt design. The framework operates independently by enforcing Markdown structure and frontmatter-driven discovery to optimize prompt clarity and just-in-time context loading.