An intelligent utility for auto-optimizing AI prompts through multi-dimensional analysis, A/B testing, and cost-reduction techniques.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-optimization-tool
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-optimization-tool 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 AI Prompt Optimizer?
AI Prompt Optimizer is a specialized tool designed to transform prompt engineering from an art into a repeatable science. By leveraging systematic evaluation and performance data, it helps developers and AI researchers refine their instructions to achieve higher quality outputs while significantly decreasing token consumption. This skill is essential for anyone looking to scale their AI operations within the Openclaw Skills framework.
The tool addresses the common pitfalls of vague instructions and inefficient structuring. It provides a data-driven approach to prompt refinement, ensuring that every interaction with an LLM is optimized for clarity, specificity, and cost-effectiveness. Whether you are building complex agents or simple automation scripts, this skill ensures your prompts are production-ready.
AI Prompt Optimizer Use Cases
- Reducing high token costs in production AI applications.
- Improving the consistency and accuracy of LLM responses for complex tasks.
- Comparing multiple prompt variations through automated A/B testing to find the highest-performing version.
- Migrating legacy prompts to more structured, role-based frameworks for better model adherence.
- Building a reusable library of high-performing prompt templates for team-wide use.
How AI Prompt Optimizer Works
- The skill analyzes the input prompt across six dimensions: clarity, specificity, structure, examples, constraints, and tone.
- It generates an overall quality score and identifies specific areas for improvement, such as missing role definitions or unclear output formats.
- Users can trigger A/B testing where the skill creates multiple variants and distributes traffic to collect performance metrics like token efficiency and user satisfaction.
- Using optimization techniques like prompt compression and few-shot injection, the skill produces an enhanced version of the prompt.
- The final optimized prompt is stored in a versioned library for easy retrieval and deployment.
AI Prompt Optimizer Setup
To get started with this skill in your environment, follow these steps:
- Install the skill via the Openclaw Skills manager:
openclaw install ai-prompt-optimizer
- Configure your API keys for the evaluation models in your global config.
- Run the optimizer using the command line or via agent triggers:
openclaw run ai-prompt-optimizer --prompt "your prompt here"
AI Prompt Optimizer Data Schema & Taxonomy
The AI Prompt Optimizer organizes performance data and metadata using the following structures:
Scoring Dimensions
| Dimension | Description | Target |
|---|---|---|
| Clarity | Are instructions explicit? | 0-100 |
| Structure | Is the format organized? | 0-100 |
| Examples | Are few-shot samples present? | 0-100 |
| Constraints | Are output limits defined? | 0-100 |
A/B Testing Metrics
output_quality: Qualitative score (1-5)token_efficiency: Tokens used per successful completiontask_completion: Percentage of successful logic executionstatistical_significance: Confidence level of the winning variant
AI Prompt Optimizer Advanced Features
- Multi-variant Generation: Automatically create up to 5 prompt variations for comprehensive testing.
- Token Compression: Drastically reduce costs by 30-60% by removing redundant linguistic fillers.
- Role-based Framework Injection: Automatically wraps simple requests into professional persona-based frameworks.
- Template Versioning: Maintain a history of optimized prompts to track performance improvements over time.
- Cross-Model Optimization: Specifically adjust prompts for different LLM architectures (e.g., OpenAI vs. Anthropic).
SKILL.md
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