Runprompt – run .prompt files from the command line
I built a single-file Python script that lets you run LLM prompts from the command line with templating, structured outputs, and the ability to chain prompts together. When I discovered Google's Dotprompt format (frontmatter + Handlebars templates), I realized it was perfect for something I'd been wanting: treating prompts as first-class programs you can pipe together Unix-style. Google uses Dotprompt in Firebase Genkit and I wanted something simpler - just run a .prompt file directly on the command line. Here's what it looks like: --- model: anthropic/claude-sonnet-4-20250514 output:…
In plain words
Runprompt is a Python command-line tool that lets users run LLM prompts stored as .prompt files, using Google's Dotprompt format with frontmatter configuration and Handlebars templating. It supports structured JSON outputs, prompt chaining, and Unix-style piping, allowing prompts to be treated as reusable programs. Designed for developers who want a lightweight alternative to Firebase Genkit, it enables straightforward integration of language model calls into shell workflows.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I built a single-file Python script that lets you run LLM prompts from the command line with templating, structured outputs, and the ability to chain prompts together. When I discovered Google's Dotprompt format (frontmatter + Handlebars templates), I realized it was perfect for something I'd been wanting: treating prompts as first-class programs you can pipe together Unix-style. Google uses Dotprompt in Firebase Genkit and I wanted something simpler - just run a .prompt file directly on the command line. Here's what it looks like: --- model: anthropic/claude-sonnet-4-20250514 output: format: json schema: sentiment: string, positive/negative/neutral confidence: number, 0-1 score --- Analyze the sentiment of: {{STDIN}} Running it: cat reviews.txt | ./runprompt sentiment.prompt | jq '.sentiment' The things I think are interesting: * Structured output schemas: Define JSON schemas in the frontmatter using a simple `field: type, description` syntax. The LLM reliably returns valid JSON you can pipe to other tools. * Prompt chaining: Pipe JSON output from one prompt as template variables into the next. This makes it easy to build multi-step agentic workflows as simple shell pipelines. * Zero dependencies: It's a single Python file that uses only stdlib. Just curl it down and run it. * Provider agnostic: Works with Anthropic, OpenAI, Google AI, and OpenRouter (which gives you access to dozens of models through one API key). You can use it to automate things like extracting structured data from unstructured text, generating reports from logs, and building small agentic workflows without spinning up a whole framework. Would love your feedback, and PRs are most welcome!
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