An expert AI agent skill designed for crafting high-performance, production-ready prompts using advanced reasoning and safety methodologies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-engineer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install prompt-engineer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Prompt Engineer Skill?
The Prompt Engineer skill is a sophisticated resource for developers aiming to maximize the utility of large language models. By integrating this into your Openclaw Skills library, you gain access to a framework that masters chain-of-thought, constitutional AI, and multi-agent system design. The skill focuses on generating reliable, safe, and business-optimized prompts that transition AI from simple chat interfaces to robust production systems.
This skill ensures that every prompt generated is not just described but provided in full, copy-pasteable blocks. It covers a wide range of model-specific optimizations for providers like OpenAI, Anthropic, and various open-source architectures, making it an essential tool for high-level AI feature development.
Prompt Engineer Skill Use Cases
- Designing complex reasoning workflows using Chain-of-Thought (CoT) and Tree-of-Thoughts patterns.
- Implementing constitutional AI to ensure safety, alignment, and self-correction in model outputs.
- Optimizing Retrieval-Augmented Generation (RAG) prompts to reduce hallucinations and improve factual accuracy.
- Developing multi-agent communication protocols for coordinated task execution.
- Creating automated evaluation frameworks to benchmark prompt performance across different LLM versions.
How Prompt Engineer Skill Works
- The agent identifies the specific use case, constraints, and target model (e.g., Claude's XML structure vs. GPT's system messages).
- It selects an appropriate prompting architecture, such as least-to-most prompting for complex decomposition or few-shot examples for pattern following.
- The skill generates a complete, standalone prompt text that can be immediately deployed.
- It attaches detailed implementation notes, including recommended parameters like temperature, top-p, and token limits.
- The workflow concludes with a structured evaluation plan, identifying potential failure modes and suggesting A/B testing strategies for the Openclaw Skills environment.
Prompt Engineer Skill Setup
To integrate the Prompt Engineer skill into your AI agent workflow, ensure the SKILL.md is available in your agent's knowledge path. You can initialize the setup by cloning the repository and linking the skill:
# Clone the skill repository
git clone https://github.com/mupeng/prompt-engineer-skill.git
# Link to your agent's active skills directory
ln -s $(pwd)/prompt-engineer-skill/SKILL.md ~/.ai-agent/skills/prompt-engineer.md
Once linked, the agent will inherit the expert persona and begin using the standardized prompt output formats.
Prompt Engineer Skill Data Schema & Taxonomy
The skill produces structured outputs following a strict taxonomy to ensure consistency across development cycles:
| Component | Description |
|---|---|
| The Prompt | A dedicated Markdown code block containing the final executable prompt text. |
| Implementation Notes | A breakdown of techniques used (CoT, PAL, etc.) and model-specific optimizations. |
| Testing & Evaluation | A list of edge cases, red-teaming scenarios, and quality metrics (accuracy, cost, latency). |
| Usage Guidelines | Instructions on variable injection, environment configuration, and version control. |
Prompt Engineer Skill Advanced Features
- Support for Program-Aided Language models (PAL) for precise computational and logic tasks.
- Specialized XML tagging strategies optimized for Anthropic Claude 3.5 Sonnet and Opus models.
- Multi-turn conversation management and context window optimization for long-form document analysis.
- Automated prompt compression techniques to reduce token costs without sacrificing performance.
- Red teaming and adversarial testing prompts to detect and prevent jailbreak attempts.
SKILL.md
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