responsible-prompting-course-llm-prompt-templates--cd3cd6fd

What This Skill Does

The responsible-prompting-course-llm-prompt-templates--cd3cd6fd skill provides a structured framework for crafting high-quality, reliable, and ethical LLM prompts. Based on the IBM Responsible Prompting Course, this skill leverages a modular template design: .join( [role, do, context, content, dont, output, assessment, iteration ] ). By breaking down complex instructions into these specific components, the skill ensures that AI outputs are predictable, context-aware, and aligned with safe prompting standards. It acts as a cognitive scaffolding tool, forcing the user to define the persona (role), the objective (do), situational background (context), core information (content), constraints (dont), formatting expectations (output), self-evaluation metrics (assessment), and recursive refinement steps (iteration).

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/hhhh124hhhh/responsible-prompting-course-llm-prompt-templates--cd3cd6fd Ensure your local environment has the necessary permissions to access the Clawhub registry.

Use Cases

This skill is ideal for:

1. Prompt Engineering: Standardizing the way teams draft complex prompts for LLMs to reduce hallucination.
2. Education: Teaching developers and data scientists how to structure requests to ensure ethical and responsible AI behavior.
3. Content Generation: Creating detailed, persona-driven outputs that require strict adherence to constraints and formatting.
4. Debugging: Using the 'assessment' and 'iteration' blocks to force the AI to double-check its own work against specific quality criteria.

Example Prompts

1. "Apply the prompt template to generate a marketing email: Use Role: Marketing Manager, Do: Draft a launch email, Context: New AI tool release, Content: Focus on productivity, Dont: Use hype-driven language, Output: Markdown, Assessment: Check for conciseness, Iteration: Refine based on feedback."
2. "Structure a coding prompt using the template: I need to write a Python script for data processing. Please define the Role as Senior Engineer and include the iteration block to review potential edge cases."
3. "Help me refine my prompt for a report summary using the IBM framework components starting with the role and ending with the assessment criteria."

Tips & Limitations

To maximize effectiveness, always define the 'dont' section clearly; specifying what the AI should not do is just as important as defining its primary objective. Note that while this template is highly effective for text generation, it may require adaptation for multi-modal tasks or complex chain-of-thought logic. Always perform a final human review on outputs generated from these templates, especially when dealing with critical enterprise workflows. The skill does not automate the actual execution; it provides the structural recipe for success.