A comprehensive technical framework for designing, optimizing, and securing high-performance LLM prompts and agentic workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-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 ai-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?
The Prompt Engineer skill for Openclaw Skills is a robust toolkit designed for AI developers building LLM-powered applications. It provides the mechanical and logical foundations required to steer large language models with precision, ensuring consistent, high-quality, and safe outputs across diverse use cases.
By integrating this skill, developers can implement advanced reasoning patterns like Chain-of-Thought (CoT) and ReAct, manage complex multi-turn conversation contexts, and enforce strict structured data schemas. It serves as a definitive guide for transforming raw model capabilities into reliable, production-ready AI services.
Prompt Engineer Use Cases
- Automating high-accuracy data extraction and classification tasks using few-shot learning.
- Building autonomous agents capable of sequential tool use and environmental interaction.
- Developing secure conversational interfaces with robust defenses against prompt injection.
- Creating evaluation frameworks to measure and maintain AI response quality and faithfulness.
- Optimizing token usage and context management for long-running AI interactions.
How Prompt Engineer Works
- Define the Foundational Layer: Establish a narrow system role, constraints, and output formats to anchor model behavior.
- Apply Pattern Engineering: Use few-shot examples or chain-of-thought triggers to demonstrate reasoning and formatting expectations.
- Orchestrate Interactions: Implement tool-use logic or multi-step agent loops (like Plan-and-Execute) for complex task solving.
- Manage Context: Utilize rolling summarization and RAG techniques to maintain relevant information within token limits.
- Enforce Safety: Apply layered guardrails and input delimiters to protect against adversarial attacks.
- Evaluate and Iterate: Run the output through an LLM-as-judge or regression test suite to ensure performance metrics are met.
Prompt Engineer Setup
Ensure you have the required binaries installed on your system. This skill requires bash, go, and python environments.
# Install the Openclaw CLI if you haven't already
curl -sSL https://openclaw.io/install.sh | sh
# Add the prompt-engineer skill to your local agent
openclaw skills add prompt-engineer
# Configure your environment variables
cp .env.example .env
# Edit .env to include your LLM provider keys
Prompt Engineer Data Schema & Taxonomy
The skill organizes its technical knowledge and operational metadata through a structured taxonomy:
| Component | Format | Description |
|---|---|---|
| System Prompts | Markdown/Text | Foundational role and constraint blocks |
| Prompt Templates | Jinja2/Handlebars | Dynamic templates with variable substitution |
| Tool Schemas | JSON/Function | API definitions and parameter descriptions |
| Eval Rubrics | YAML/JSON | Pass/fail criteria for regression testing |
| Memory Store | Structured Text | Persistent user facts and conversation summaries |
Prompt Engineer Advanced Features
- Multi-modal analysis support for UI, diagrams, and chart interpretation.
- ReAct and Tree-of-Thought agent architectures for autonomous problem-solving.
- Self-correction and reflection loops for high-stakes decision-making.
- Advanced context compression and sliding window memory management.
- Robust safety protocols including instruction hierarchy and PII redaction.
SKILL.md
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