SEC.01

The playbook

The run starts read-only: the recon probes decide what happens next. The steps that follow are adapted to the distribution and state found — a real machine is rarely clean.

1. 01recondistro, version and package manager (apt or dnf)
2. 02reconDocker Engine + compose plugin present and running? (otherwise, docker step)
3. 03reconJupyterLab already deployed (container, volume, /opt/jupyter)? healthy → verify and skip
4. 04reconport 8888 free?
5. 05reconRAM ≥ 2 GB and ≥ 5 GB of free disk
6. 06actInstall Docker Engine + Compose (only if missing)verify
7. 07actGenerate the access token (/opt/jupyter/jupyter.env)verify
8. 08actStart JupyterLabverify
9. 09actThe API responds (token required)verify

SEC.02

What it verifies

Each step ships its own check. The run only moves on when the check passes, so what the recipe reports as done is what is actually on the server.

1. 01verifyInstall Docker Engine + Compose (only if missing)Verified bysystemctl is-active docker && docker compose version
2. 02verifyGenerate the access token (/opt/jupyter/jupyter.env)Verified bystat -c '%a' /opt/jupyter/jupyter.env && grep -c '^JUPYTER_TOKEN=.' /opt/jupyter/jupyter.env
3. 03verifyStart JupyterLabVerified bydocker inspect -f '{{.State.Status}}' jupyter
4. 04verifyThe API responds (token required)Verified bytimeout 120 bash -c 'until curl -s -o /dev/null http://127.0.0.1:8888/api; do sleep 3; done'; curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:8888/api/contents

SEC.03

Guardrails

Non-negotiable instructions the run must respect, whatever it finds on the server.

SEC.04

How a recipe runs in Servor

Servor is zero-knowledge: the platform cannot execute anything on its own. When you launch this recipe, a run reads the reconnaissance, plans the steps and asks for your approval. Each approved command is signed by your browser — with a key the server never sees — then relayed verbatim to the agent, which verifies the signature locally before executing.

SEC.05

Deploy on Servor button

Maintain JupyterLab or a project that needs it? Put this button in your README or on your site: one click and your users install it on their own server.

SEC.06

More ai / ml recipes

1. 01OllamaOllama to run local LLMs (NVIDIA GPU if present, CPU otherwise), API on 127.0.0.1, optional model pull.
2. 02Open WebUIOpen WebUI, a web interface for LLMs (Ollama), via Docker on 127.0.0.1, wired to the local Ollama.
3. 03NVIDIA drivers + CUDANVIDIA driver, CUDA toolkit and Docker Container Toolkit from the official repositories. Destructive: kernel modules, restart likely.
4. 04ComfyUI (Stable Diffusion)ComfyUI for image generation, installed from the official repository as a systemd service (NVIDIA GPU recommended, CPU possible).
5. 05vLLM inference servervLLM to serve an LLM through an OpenAI-compatible API on 127.0.0.1, protected by a generated key. NVIDIA GPU required.