prompts

What This Skill Does

The prompts skill provides a rigorous, engineering-focused framework for developing and maintaining LLM interactions. It treats prompts as natural-language source code, requiring specific input-output definitions, versioning, and testing criteria. By moving away from "trial-and-error" prompting toward a structured six-stage workflow—covering task definition, constraint enforcement, few-shot design, evaluation set construction, disciplined iteration, and production monitoring—this skill enables developers to ship high-reliability AI features with confidence. It ensures that system messages and user inputs are modularized, reproducible, and resilient against regressions.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/clawkk/prompts

Use Cases

• Structured Output Generation: Perfect for enforcing strict JSON schemas or tool-use protocols for downstream API integration.
• Quality Assurance: Use when you need to prevent model drift or hallucinations in sensitive business processes.
• Complex Reasoning Chains: Deploy this for tasks requiring multi-step, logic-heavy workflows where chain-of-thought is critical.
• Prompt Versioning: Maintain a history of prompt iterations to easily roll back changes that cause performance degradation in production.

Example Prompts

1. "I am struggling with the output format of my customer support bot; please help me define a strict JSON schema and a set of constraints to ensure it only answers within our internal documentation."
2. "Let's start the prompt engineering workflow for a new RAG system. We need to define the task rubric and build an initial evaluation set of 20 questions to test grounding against our knowledge base."
3. "My model is hallucinating citations. Help me refactor my current system prompt to include a mandatory citation check and define an evaluation set that specifically tests for source accuracy."

Tips & Limitations

• Clarity over Cleverness: Avoid prompt "hacks." Direct, explicit instructions typically outperform complex, clever phrasing.
• Keep Examples Concise: While few-shot prompting is powerful, avoid bloating the prompt with excessive examples that might confuse the model. Focus on high-quality, diverse examples rather than quantity.
• Decouple Logic: Always separate system-level policies from user-level task instances.
• Limitations: Note that this skill manages the workflow and design of prompts. For large-scale automated evaluation harnesses, consider pairing this with the llm-evaluation skill.