Execute local prompts in SSH remote shells
Instead of giving LLM tools SSH access or installing them on a server, the following command: $ promptctl ssh user@server makes a set of locally defined prompts "magically" appear within the remote shell as executable command line programs. For example, I have locally defined prompts for `llm-analyze-config` and `askai`. Then on (any) remote host I can: $ promptctl ssh user@host # Now on remote host $ llm-analyze-config /etc/nginx.conf $ cat docker-compose.yml | askai "add a load balancer" the prompts behind `llm-analyze-config` and `askai` execute on my local computer (even though…
What it does
In the maker’s words, at launch
Instead of giving LLM tools SSH access or installing them on a server, the following command: $ promptctl ssh user@server makes a set of locally defined prompts "magically" appear within the remote shell as executable command line programs. For example, I have locally defined prompts for `llm-analyze-config` and `askai`. Then on (any) remote host I can: $ promptctl ssh user@host # Now on remote host $ llm-analyze-config /etc/nginx.conf $ cat docker-compose.yml | askai "add a load balancer" the prompts behind `llm-analyze-config` and `askai` execute on my local computer (even though they're invoked remotely) via the llm of my choosing. This way LLM tools are never granted SSH access to the server, and nothing needs to be installed to the server. In fact, the server does not even need outbound internet connections to be enabled. Github: https://github.com/tgalal/promptcmd/
Does the same job
all alternatives →- PLPrompting LLMs in Bash scripts2025 · elijahpotter.dev · ▲79
https://github.com/elijah-potter/ofc

- GAGet answers for shell commands from GPT3 from your terminal2022 · github.com · ▲159
I was constantly googling CLI commands so I built this small CLI tool with GPT3. You can ask for shell commands right from the CLI. You'd need to use your own API KEY for this but it's pretty simple, instructions are in the README Not perfect but not bad either.
- LTLlm2sh – Translate plain-language requests into shell commands2024 · github.com · ▲67
This is my take on the common "use llms to generate shell commands" utility. Emphasis is placed on good CLI UX, simplicity, and flexibility. `llm2sh` supports multiple LLM providers and lets LLMs generate multi-command sequences to handle complex tasks. There is also limited support for commands requiring `sudo` and other basic input. I recommend using Groq llama3-70b for day-to-day use. The ultra-low latency is a game-changer - its near-instant responses helps `llm2sh` integrate seamlessly into day-to-day tasks without breaking you out of the 'zone'. For more advanced tasks, swapping to…

- RRRunprompt – run .prompt files from the command lineNov 2025 · github.com · ▲134
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:…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, March 2026
the whole month →

Switch from ChatGPT to Claude with import memory feature
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