An advanced AI prompting skill designed to help you architect, test, and diagnose high-performing system prompts and user instructions across major LLMs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mimo-prompt-engineering-expert
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 mimo-prompt-engineering-expert 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 Engineering Expert?
The Prompt Engineering Expert skill is a structured workflow designed to elevate how you interact with Large Language Models. Instead of relying on rigid, outdated templates, this skill teaches you the core structural mechanics of prompt architecture using the Role-Task-Format-CoT methodology. It helps you design consistent, reliable instructions tailored for any model, including OpenAI's GPT, Anthropic's Claude, Google's Gemini, and other open-source alternatives.
By leveraging this skill on Openclaw Skills, developers and content creators can systematically eliminate vague AI responses, formatting errors, and hallucinations. This capability acts as an interactive diagnostic and optimization hub, ensuring your AI agents perform with maximum deterministic efficiency.
Prompt Engineering Expert Use Cases
- When your current AI prompts produce inconsistent outputs or fail to follow structural formatting like JSON.
- When you need to design complex, multi-step agent behaviors utilizing Chain-of-Thought (CoT) or ReAct reasoning frameworks.
- When adapting prompts across different LLM backends (e.g., migrating from Claude's XML style to GPT's markdown style).
- To systematically debug and troubleshoot bad AI behaviors using an integrated 12-point diagnostics checklist.
How Prompt Engineering Expert Works
- Requirement Diagnostics: The skill analyzes your specific task type (such as Q&A, synthesis, classification, or data extraction) and maps output constraints.
- Role Formulation (Role): It creates a precise professional identity, boundary constraints, tone parameters, and safety guardrails for the model.
- Task Decomposition (Task): It breaks down complex instructions into sequential, prioritized, and quantifiable execution steps.
- Few-Shot Construction (Few-shot): It assists in building 1-3 highly accurate input-output examples, covering both standard and edge cases.
- Output Control & Constraints: The skill enforces strict formatting rules (JSON, Markdown, tables) and applies negative constraints.
- Advanced Optimization & Testing: It runs evaluation simulations, ensures Chain-of-Thought consistency, and documents the finalized prompt assets.
Prompt Engineering Expert Setup
To get started with the Prompt Engineering Expert skill on your Openclaw Skills workspace, configure your environment or install it using your agent configuration pipeline:
# Install or enable the skill in your Openclaw workspace
claw skill install prompt-engineering-expert
Ensure that you have your target LLM API keys configured to test the generated prompts dynamically during development.
Prompt Engineering Expert Data Schema & Taxonomy
| Component | Format | Description |
|---|---|---|
| Role Setting | Text / Markdown | Defines AI identity, boundaries, and behavioral safety guardrails |
| Task Specifications | Step-by-Step List | Detailed, prioritized execution actions to take |
| Examples (Few-shot) | JSON / XML / Key-Value | 1 to 3 curated positive/negative samples for formatting consistency |
| Output Controls | Markdown Schema | Defines constraints like JSON structure, length, and negative constraints |
| Advanced Reasoning | Chain-of-Thought / ReAct | Instructs the model on explicit reasoning steps before output generation |
Prompt Engineering Expert Advanced Features
- 12-Point Diagnostic Table: Instantly pinpoint prompt failures, diagnose model-specific deviations, and receive automated correction patches.
- Multi-Model Adaptation Guides: Optimize prompts natively for 7 major model families using distinct structural patterns (e.g., XML tags for Claude).
- Anti-Rationalization Engine: Ensures strict engineering standards, avoiding lazy shortcuts like 'relying purely on a smarter model' or neglecting test iterations.
- Standardized Output Schema: Automatically structures final prompt specifications into a clean, reusable template block ready for copy-pasting into production workflows.
SKILL.md
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