# AI use cases for Sno Station (sno-ai/sno-station)

Repo: [sno-ai/sno-station](https://github.com/sno-ai/sno-station) | Use cases: 4 | Page: https://dedupedai.com/use-cases/sno-ai-sno-station/

Sno Station is an open source tool that runs on your computer and lets Codex, Claude Code, Cursor, Hermes Agent and OpenClaw share one encrypted memory, hand work to each other, review each other's work, and run an overnight Self Learning step.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Hand off a half-finished task when one agent hits its limit: https://dedupedai.com/use-cases/sno-ai-sno-station/hand-off-work-when-an-agent-runs-out/
- Get a code review from a different agent: https://dedupedai.com/use-cases/sno-ai-sno-station/get-a-second-opinion-from-another-agent/
- Review the skill changes Self Learning proposes each morning: https://dedupedai.com/use-cases/sno-ai-sno-station/review-overnight-skill-proposals/
- Add Cursor to a shared memory with Codex and Claude Code: https://dedupedai.com/use-cases/sno-ai-sno-station/add-cursor-to-the-shared-agent-memory/

## How to hand off a half-finished task when one agent hits its limit with Sno Station

Repo: [sno-ai/sno-station](https://github.com/sno-ai/sno-station) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Another agent picks up the same task from shared memory with context intact.

**Who it is for:** A developer mid-task in one agent who hits a rate limit or cap and does not want to re-explain the work to a second agent.

**The problem:** When one agent runs out of quota partway through a task, the work stalls or has to be pasted again into another agent.

**The outcome:** The second agent continues the task from the same memory and mailbox, with no pasting.

### Steps

1. Run `sh -c 'sno_installer_body=$(curl -fsSL https://sno.ai/install) && printf "%s\n" "$sno_installer_body" | sh'` in a terminal.
2. In any supported agent conversation, say `Sno onboard` and follow the walkthrough.
3. When the first agent hits its cap, say `sno reach call` from the other agent.
4. Confirm the second agent reports the task and the prior context from the shared memory.

### Prompt to paste into your agent

```text
In the sno-station repository, use `sno reach call` to hand the current task from the agent at <placeholder> to the other agent. Expected output: the receiving agent restates the task and the last completed step from shared memory. Verify it does not restart the task from the beginning.
```

### Caveat

The README shows the hand-off in a multi-agent setup but does not describe how the receiving agent confirms a hand-off, so check the result yourself.

Risk, remote script: The install command downloads a script from sno.ai and runs it in your shell, so review it first if you do not trust the source.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sno-ai-sno-station/hand-off-work-when-an-agent-runs-out/

## How to get a code review from a different agent with Sno Station

Repo: [sno-ai/sno-station](https://github.com/sno-ai/sno-station) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Ask one agent to review another agent's work, so the reviewer does not share its habits.

**Who it is for:** A developer who wants a check on code an agent just wrote, without relying on the same agent to grade itself.

**The problem:** An agent reviewing its own output tends to agree with it, so the review adds little.

**The outcome:** A review from a different agent that is likely to catch what the first one missed.

### Steps

1. Run `sh -c 'sno_installer_body=$(curl -fsSL https://sno.ai/install) && printf "%s\n" "$sno_installer_body" | sh'` in a terminal.
2. In any supported agent conversation, say `Sno onboard` and follow the walkthrough.
3. Ask one agent to `peer-review` the work the other agent produced.
4. Read the review and check each finding against the code before accepting it.

### Prompt to paste into your agent

```text
In the sno-station repository, ask the <placeholder> agent to peer-review the latest changes made by the other agent. Expected output: a review written by the reviewer, not the author. Verify that the reviewer names the specific files and lines it checked.
```

### Caveat

The README says the reviewer is always a different agent but does not show the exact review output format, so confirm findings yourself.

Risk, remote script: The install command downloads a script from sno.ai and runs it in your shell, so review it first if you do not trust the source.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sno-ai-sno-station/get-a-second-opinion-from-another-agent/

## How to review the skill changes Self Learning proposes each morning with Sno Station

Repo: [sno-ai/sno-station](https://github.com/sno-ai/sno-station) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Read the nightly proposals and accept only the ones you want applied to your agents' skills.

**Who it is for:** A developer who wants agents to stop repeating the same mistakes but wants to approve every change to their skills.

**The problem:** Agents keep repeating the same errors across sessions, and no one has time to write the fixes into their skill files.

**The outcome:** Recurring mistakes become proposed skill changes, and only the ones you accept take effect.

### Steps

1. Run `sh -c 'sno_installer_body=$(curl -fsSL https://sno.ai/install) && printf "%s\n" "$sno_installer_body" | sh'` in a terminal.
2. Let Self Learning run overnight, which the README describes as every night.
3. In the morning, review the proposals that Self Learning produced.
4. Run `sno rem-reflect accept` for each proposal you like.

### Prompt to paste into your agent

```text
In the sno-station repository, review the proposals from the last Self Learning run and accept only the ones I approve using `sno rem-reflect accept`. Expected output: a list of accepted and rejected proposals. Verify that no skill changed for a proposal you did not accept.
```

### Caveat

The README says REM Enhanced mode sends your memory text to Sno's cloud, while Local First mode uploads nothing, so pick the mode you want in settings before relying on the nightly run.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sno-ai-sno-station/review-overnight-skill-proposals/

## How to add Cursor to a shared memory with Codex and Claude Code with Sno Station

Repo: [sno-ai/sno-station](https://github.com/sno-ai/sno-station) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Connect Cursor so its chats use the same memory, skills and review flow as your other agents.

**Who it is for:** A developer on Linux or macOS who already uses Codex or Claude Code and also works in Cursor's agent chat or command line.

**The problem:** Cursor starts each chat without the context and skills that the other agents on the same machine already use.

**The outcome:** Cursor reads the shared skills, its prompts are remembered, and it can take over or be reviewed like the other agents.

### Steps

1. Run `cursor-agent login` once in a terminal.
2. Run `sh -c 'sno_installer_body=$(curl -fsSL https://sno.ai/install) && printf "%s\n" "$sno_installer_body" | sh'` to install Sno and connect the agents it finds.
3. If you installed another agent later, run `sno setup` again.
4. Open a Cursor chat and send a prompt, then confirm it shows the shared brief for the repository.

### Prompt to paste into your agent

```text
In the sno-station repository, start a Cursor chat on a Linux or macOS machine and ask it to summarise the shared memory for <placeholder>. Expected output: a short summary that reflects work done in another agent. Verify that the answer mentions context not visible in the Cursor chat alone.
```

### Caveat

The README says Windows, the Cursor app on Linux and Cursor cloud agents are not supported, and in the Cursor app the brief arrives with the first prompt of a new window.

Risk, remote script: The install command downloads a script from sno.ai and runs it in your shell, so review it first if you do not trust the source.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sno-ai-sno-station/add-cursor-to-the-shared-agent-memory/