Generate dynamic prompts using Handlebars templates and TypeScript utilities.
npx skills add https://github.com/phatblat/dotfiles --skill prompting-phatblat
Please help me install this Agent Skill. Skill: Prompting Source: https://github.com/phatblat/dotfiles/tree/main/.claude/skills/Prompting Command: npx skills add https://github.com/phatblat/dotfiles --skill prompting-phatblat
This Skill streamlines prompt engineering by providing a robust system for dynamic prompt generation, optimization, and composition, ensuring AI interactions are efficient and effective.
Use the prompting skill to render the 'Primitives/Briefing.hbs' template with provided agent and task data.
High-intent search queries and answers about installing and using this skill.
Meta-prompting generates complex AI prompts programmatically by combining standard templates with specific task details and variables. This technique streamlines prompt engineering by automating the composition of structured, reusable prompt structures for agents and workflows.
Meta-prompting improves LLM composition by applying context engineering principles and Claude 4.x best practices to programmatically assemble prompt components. This method enhances AI response quality and token efficiency through structured, template-driven generation.
YAML parsing is required for data management alongside Handlebars templating for rendering. The system uses YAML to manage standards and structured data inputs, enabling programmatic composition of prompt variables for dynamic generation.
The prompt engineering approach adheres to Anthropic's Claude 4.x best practices and context engineering principles. It applies these standards to optimize AI response quality, ensure token efficiency, and guide dynamic prompt generation.
The best way to create reusable prompt structures is through template generation using Handlebars. By rendering standard templates with specific agent personality traits and task details, you can efficiently produce specialized agent briefings and workflows.
Programmatic prompt optimization is needed when AI interactions require improved response quality and token efficiency. It leverages best practices and research to dynamically refine prompts, ensuring effective communication with large language models.
verrio1Render Handlebars templates with YAML/JSON data to generate prompts.
bugrabilgeOptimize LLM prompts using Constitutional AI, Chain-of-Thought, and Few-Shot Learning.
blacktopGenerate optimized handoff prompts for delegating tasks to LLM agents.
RooseveltAdvisorsGenerate and validate prompts using Handlebars templates with YAML data.
