A high-performance CLI tool for benchmarking AI prompt costs, token usage, and model performance across multiple providers.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install singleshot-prompt-testing
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install singleshot-prompt-testing using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Singleshot is a specialized utility designed for developers to refine and validate AI prompts before production deployment. It provides granular metrics on token consumption, estimated costs, and response times, supporting major providers like OpenAI, Anthropic, and local models via Ollama. By integrating this tool into your Openclaw Skills development workflow, you can ensure your agent interactions are both cost-effective and performant.
The tool allows for rapid experimentation using single-shot chat commands or configuration files, making it an essential part of the modern AI development lifecycle. Whether you are aiming to reduce overhead or improve the accuracy of a system prompt, Singleshot provides the empirical data needed to make informed decisions.
# Install via Homebrew
brew tap vincentzhangz/singleshot
brew install singleshot
# Or install via Cargo
cargo install singleshot
# Configure environment variables for authentication
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
The skill generates detailed Markdown reports structured to facilitate easy parsing and comparison of Openclaw Skills performance.
| Section | Description |
|---|---|
| Token Usage | Breaks down Input, Output, and Total tokens for the session. |
| Cost (Estimated) | Calculates estimated USD cost based on current provider pricing. |
| Timing | Captures Time to First Token (TTFT) and Total response time. |
| Config Files | Supports .md files with structured headers for provider and model settings. |
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