A model-agnostic benchmarking tool that compares performance, cost, and quality across nine major AI providers simultaneously.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-performance-tester
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-performance-tester 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 Performance Tester?
The Prompt Performance Tester is a professional-grade benchmarking utility designed to bring transparency to the rapidly evolving LLM landscape. As a key component of the Openclaw Skills ecosystem, it allows developers to pass any model ID and automatically detect the provider to measure critical metrics like latency, exact API costs, and response quality. It supports a wide array of providers including Anthropic, OpenAI, Google, DeepSeek, and Mistral, making it an indispensable tool for model selection.
By using this skill, teams can move away from manual API testing and embrace a systematic approach to prompt engineering. This tool from the Openclaw Skills library provides clear, data-driven recommendations, helping you decide whether a high-reasoning model like Claude Opus or a cost-effective alternative like Gemini Flash-Lite is best for your specific application requirements.
Prompt Performance Tester Use Cases
- Production Model Selection: Compare multiple models to find the perfect balance between response speed and cost before deployment.
- Prompt Engineering: Validate how different system prompts perform across various model architectures to ensure output consistency.
- Budget Management: Analyze potential monthly savings by identifying cheaper alternatives that maintain high quality scores.
- Infrastructure Monitoring: Benchmark API latency and error rates across different providers to ensure high availability.
How Prompt Performance Tester Works
- The user inputs a prompt and selects a list of model IDs to benchmark.
- The system utilizes prefix-based routing to auto-detect the correct provider for each model without requiring hardcoded lists.
- SDK clients are initialized lazily, loading only the necessary libraries for the providers being tested.
- The skill executes the prompt across all selected models, often performing multiple runs to ensure statistical consistency.
- Real-time telemetry is captured, including millisecond-precise latency and exact token counts for cost calculation.
- A comprehensive comparison report is generated, offering smart recommendations based on the collected performance data.
Prompt Performance Tester Setup
To start using this tool, subscribe via the registry and set the environment variables for the providers you intend to test.
# Example for key providers
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."
export GOOGLE_API_KEY="AI..."
Install the necessary dependencies based on your testing needs:
pip install anthropic openai google-generativeai mistralai
Prompt Performance Tester Data Schema & Taxonomy
The skill produces a structured data output capturing the following metrics for every test run:
| Metric | Type | Description |
|---|---|---|
| Latency | Integer | Total response time in milliseconds |
| Cost | Float | Exact USD cost calculated from input and output tokens |
| Quality | Integer | Heuristic score (0-100) based on length and completeness |
| Tokens | Object | Breakdown of input and output token counts |
| Consistency | Float | Variance observed across multiple test iterations |
Prompt Performance Tester Advanced Features
- Model-Agnostic Prefix Routing: Automatically supports new models as they are released by detecting provider prefixes like gpt-, claude-, or gemini-.
- Multi-Provider Cost Analysis: Built-in pricing tables for over 20 models to provide instant financial forecasting.
- Automated Recommendation Engine: Logic-based insights that identify the fastest, cheapest, and highest-quality models for your specific prompt.
- CI/CD Compatibility: Can be integrated into automated pipelines as a Python module or CLI tool to ensure prompt quality within the Openclaw Skills framework.
SKILL.md
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