Singleshot Prompt Testing

What This Skill Does

The Singleshot Prompt Testing skill, developed by vincentzhangz, provides an essential toolkit for developers and AI engineers to benchmark, optimize, and validate LLM prompts before deploying them into production environments. Instead of guessing how a prompt will perform or how much it will cost, this skill allows users to execute targeted, single-shot tests across various providers (OpenAI, Anthropic, Ollama). It provides granular metrics, including token usage, estimated costs, and latency, directly into structured Markdown reports. By standardizing the testing workflow, the tool empowers users to minimize expenses while maximizing output quality, making it a critical asset for maintaining cost-efficient AI agent architectures.

Installation

To install this skill via OpenClaw, use the following command: clawhub install openclaw/skills/skills/vincentzhangz/singleshot-prompt-testing

Alternatively, you can install the underlying binary directly: brew tap vincentzhangz/singleshot brew install singleshot

Use Cases

• Prompt Refinement: Systematically testing variations of a prompt to see which yields higher quality outputs.
• Cost Optimization: Benchmarking prompt costs across different providers and models (e.g., comparing gpt-4o vs gpt-4o-mini) to select the most economical model that satisfies performance requirements.
• Latency Auditing: Measuring 'Time to First Token' and total request duration to ensure your AI agents remain responsive for end-users.
• Production Validation: Using the tool as a sandboxed environment to dry-run prompts against real inputs before hardcoding them into production agent workflows.

Example Prompts

1. "Run a baseline test for my customer support prompt using gpt-4o-mini and save the report to support-v1.md with full detail metrics."
2. "Compare the cost and token count of this prompt between Claude 3.5 Sonnet and GPT-4o, generating a comparative report for each."
3. "Test my optimized system prompt against the latest input data and check if the total cost stays under $0.0002 per request."

Tips & Limitations

To get the most out of this skill, always leverage the -d (detail) and -r (report) flags; without these, you will miss the diagnostic data necessary for optimization. When testing, iterate by reducing system prompt verbosity and testing with local LLM providers like Ollama to keep development costs at zero. Note that this skill is designed for single-shot testing and is not intended for managing long-term conversation history or stateful agent memory. Ensure you review the generated report files regularly to catch unexpected token spikes or latency bottlenecks.