prompt-engineering

What This Skill Does

The prompt-engineering skill is an advanced toolkit designed to help users interact effectively with state-of-the-art AI models, including LLMs like Claude and GPT-4, as well as generative media models like FLUX and Stable Diffusion. This skill abstracts complex prompt engineering methodologies—such as chain-of-thought reasoning, few-shot learning, and structured system prompting—into actionable commands via the inference.sh CLI. By standardizing how you communicate with AI, this skill ensures more consistent, reliable, and high-quality outputs across a variety of complex tasks.

Installation

You can integrate this skill into your environment using the following command:

clawhub install openclaw/skills/skills/okaris/prompt-engineering

Ensure you have the inference.sh CLI installed. You can set it up by running curl -fsSL https://cli.inference.sh | sh and then authenticating with infsh login. This lightweight binary management ensures secure and efficient communication with supported AI models.

Use Cases

• Software Development: Use system roles to have models act as senior code reviewers or security auditors.
• Data Analysis: Employ chain-of-thought prompting to break down complex mathematical or logical problems into manageable steps.
• Content Creation: Utilize few-shot prompting to force models to adopt specific tone, style, or business-formal output formats.
• Debugging: Transform vague problem statements into high-context prompts to receive accurate code fixes.

Example Prompts

1. "Apply chain-of-thought reasoning to explain why my Python loop is returning a zero total even when even numbers are present in the list."
2. "Acting as a professional copywriter, use this few-shot structure to rewrite my draft into an empathetic yet professional customer service response."
3. "Structure a system prompt for a FLUX image generation request that includes specific negative prompts to ensure photorealistic lighting and no distortion."

Tips & Limitations

To maximize effectiveness, always prioritize clarity. A well-structured prompt follows the [Role] + [Task] + [Constraints] + [Output Format] paradigm. Be aware that excessive few-shot examples can increase token costs and latency. Additionally, while this skill optimizes prompt structure, it cannot bypass the inherent limitations or safety guardrails of the underlying AI models. Always verify generated outputs for accuracy, especially in high-stakes technical or legal contexts.