Prompt Performance Tester

What This Skill Does

The Prompt Performance Tester is a robust diagnostic tool designed for AI engineers and product developers to evaluate LLM behavior systematically. Instead of guessing which model performs best for a specific prompt, this tool executes your input across Anthropic, OpenAI, and Google models concurrently. It captures critical performance telemetry, including round-trip latency, precise API token costs, response quality metrics, and output consistency. By generating side-by-side comparisons, it eliminates the guesswork involved in model selection, allowing you to optimize for either peak intelligence or maximum cost-efficiency.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/vedantsingh60/prompt-performance-tester

Ensure your environment variables are configured with your respective API keys for Claude, OpenAI, and Gemini before triggering the first test run.

Use Cases

• Model Benchmarking: Determine the exact crossover point where a more expensive model provides diminishing returns on quality for your specific data sets.
• Cost Optimization: Identify the most affordable model that still meets your quality threshold, potentially reducing monthly infrastructure spend by over 90%.
• Latency Tuning: Find the best 'instant' or 'flash' model for real-time customer support chatbots where response time is the primary user experience driver.
• Regression Testing: Ensure that model updates (e.g., from GPT-5.1 to 5.2) do not negatively impact your production prompts.

Example Prompts

1. "Test the prompt 'Summarize this technical article in 3 bullet points' against all 10 supported models and rank them by cost-per-quality ratio."
2. "Perform a performance test on the following prompt: 'Draft a Python script to scrape a website using Selenium' and compare the latency between Claude 4.5 Sonnet and GPT-5.2-Thinking."
3. "Evaluate the consistency of the models by running the prompt 'Explain quantum entanglement to a five-year-old' five times each and report the variance in response quality."

Tips & Limitations

• Token Variance: Be mindful that output length can fluctuate significantly between models for the same prompt, which impacts the final cost analysis.
• API Keys: This tool requires valid API keys for all services tested. If one service is missing, the tool will gracefully skip those models in the report.
• Rate Limits: When testing across all 10 models simultaneously, be aware of your provider rate limits to avoid unintended throttling.
• Quality Scores: The quality score is generated via an internal meta-model; for highly specific technical tasks, ensure your prompt includes clear success criteria to make the scoring more objective.