prompt-engineer

What This Skill Does

The prompt-engineer skill is a sophisticated toolkit for designing, optimizing, and deploying system prompts for complex AI agent systems. It leverages a rigorous three-layer architecture—Foundation, Structure, and Execution—to move beyond basic prompt drafting. By integrating industry-proven patterns from advanced frameworks like Claude Code, Vercel v0, and Manus, this skill ensures that your agents exhibit predictable behavior, robust tool-use capabilities, and high-fidelity output. It operates across four core modes: Build, Iterate, Diagnose, and Explain, allowing you to create prompts from scratch or refine underperforming legacy instructions. The skill utilizes modular extensions to ground responses in specific contexts, such as RAG-based document retrieval, domain-specific calibration for high-stakes environments, and orchestration logic for multi-agent systems. It essentially functions as a prompt architect in your terminal, translating high-level intent into highly structured, machine-readable specifications that minimize hallucinations and maximize reliability.

Installation

To integrate the prompt-engineer skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/iops/prompt-engineer-agentic

Ensure that you have the latest version of OpenClaw installed to maintain compatibility with the modular routing system.

Use Cases

• Agent Specification: Rapidly generate comprehensive system prompts for autonomous agents, including tool schemas, step-by-step reasoning chains, and error-handling protocols.
• Confidence-Graded Advisory: Design advisory AI systems that output their confidence levels alongside recommendations, ideal for financial, medical, or legal applications.
• Prompt Optimization: Diagnose failures in existing prompts by analyzing the interaction between system instructions and model execution, providing actionable feedback for improvement.
• Multi-Agent Orchestration: Create complex prompt architectures for multi-agent systems where specialist agents need to hand off tasks, share context, and resolve conflicts.
• Tooling Integration: Draft precise natural language specifications for function calls, ensuring that AI agents understand how to trigger and utilize external tools effectively.

Example Prompts

1. "I need a system prompt for a financial research agent. It needs to cite sources from my local knowledge base and include a confidence score for its insights. Can you build this?"
2. "My current code-generation agent is failing to handle tool errors properly. Can you diagnose my system prompt and suggest a more robust execution structure?"
3. "Write a spec for a multi-agent system where a 'Researcher' agent gathers data, a 'Summarizer' agent compiles it, and an 'Editor' agent finalizes the report."

Tips & Limitations

• Start with the Spec: When dealing with complex agents, always start by asking the skill to 'build a spec'. This ensures the Foundation layer is correctly mapped before moving to specific execution instructions.
• Iterative Refinement: Don't expect the first prompt version to be production-ready. Use the 'Iterate' mode to tweak constraints and persona instructions based on initial test runs.
• Context is King: The more information you provide about your target model (e.g., Claude 3.5 Sonnet, GPT-4o) and your specific domain, the better the skill can calibrate the prompt structure.
• Limitations: While this skill excels at prompt architecture, it cannot substitute for ground-truth testing. Always perform rigorous evaluations in your staging environment before deploying to production.