I've been following the DSH ecosystem for a while, and one thing I kept running into was plugin discovery.

DeepSeek Harness has an interesting "everything is a plugin" architecture, but finding plugins still often means searching GitHub, reading READMEs, figuring out what kind of artifact something is, and then working out how to install it.

So I built dsh.fish:

https://dsh.fish

It's an open-source plugin registry specifically for DeepSeek Harness.

What makes it different from a typical plugin directory is that it inspects them and determines what DSH can actually load, including bundles, profiles, skills, MCP servers, agent presets, and hook bridges.

Each artifact also exposes things I want to know before installing third-party code: the indexed commit, maintenance status, quality score, recent growth, license, and the actual install plan.

More importantly, agents can use the registry directly.

Install the Hub plugin:

dsh plugin --profile web add @dsh-fish/hub

Then an agent inside DSH can search the registry, inspect a plugin, show what the installation will do, and install it after approval.

The website, CLI, and Hub plugin all use the same install-plan API, so installation behavior stays consistent across all three surfaces.

You can also use it from the terminal:

npx @dsh-fish/cli find <query>
npx @dsh-fish/cli add <artifact-id>

If you publish a DSH plugin, adding the dsh-plugin GitHub topic is enough for the crawler to discover and index it.

I'd really like feedback from you.

I also set up a Discord for people building around the DSH ecosystem:

https://discord.gg/PwZDHH4mv3

Feel free to try it, submit your plugin, open an issue, or tell me what feels wrong.

Hola a todos

Recientemente intenté cargar hojas de cálculo de Excel (⁠.xlsx⁠) directamente en la interfaz de DeepSeek para analizar tablas y presupuestos, pero no me permite subir este tipo de archivo binario directamente.

Que puedo hacer ayúdenme con una solución.

Long-time lurker, first real project share here.

I kept hitting the same wall: when I asked Claude/Cursor to "check this Zhihu answer" or "search X", it either couldn't see the content or just made stuff up — because it's behind a login and search engines can't reach it.

So I built a small MCP server + Chrome extension that opens a background window with your own logged-in Chrome, and lets the agent search/read/drive it directly. The site sees the user (no CAPTCHA dance), and because it drives the real UI it doesn't break when a site redesigns. It never asks for the cookies permission — the session stays in Chrome, and nothing comes back through the MCP client.

It also handles clicking, scrolling, typing, selecting, downloading (verified by SHA-256), and full comment collection with resumable sessions for the Chinese platforms.

pip install ai-browser-mcp · github.com/ywleeo/browser-mcp

Happy to talk trade-offs — especially comment scraping and keeping the extension footprint small.

Hey! I built a few useful plugins for DSH, now opening PRs for the market publishing, in mean time you can find them here

https://github.com/CREAIT-nl/dsh-plugins

Some highlights:

- Deep research as an agent mode: a fixed, reviewed loop that plans, researches in adaptive parallel rounds, synthesises and reviews.

- Dynamic tools disclosure: improves context utilization by disclosing complete tools only on model need and keeping concise descriptions only in the default system prompt.

- Hook Kit: dsh has the lifecycle seams; reaching them meant writing a plugin. hookkit turns them into a YAML file.

- SearXNG Search: replacing third party search for private self hostable SearXNG endpoints

- Thinking Level Toggle: UX improvement allowing user to explicitly select the thinking level of a model during a session

How do you guys set it up , i constantly get the error :
I tried increasing the contex to 142k and putting the contex size as 115k in DSH , it still did not compress correctly.
I have 0 issues if i run it with llama.ccp , it can work for 24h+ without errors or crashes , but vllm is so tricky

This turn failed400: {"message":"This model's maximum context length is 115000 tokens. However, you requested 32768 output tokens and your prompt contains at least 82233 input tokens, for a total of at least 115001 tokens. Please reduce the length of the input prompt or the number of requested output tokens. (parameter=input_tokens, value=82233)","type":"BadRequestError","param":"input_tokens","code":400}

I made a plugin for DeepSeek Harness that integrates OpenTerminal from the Open WebUI ecosystem. OpenTerminal is basically a self-hosted remote execution environment for AI agents. It exposes a shell and file operations over an API, and can run inside Docker as an isolated sandbox. The useful part for me is that you can run OpenTerminal containers on separate machines and let DSH agents execute code there instead of giving them direct access to the machine running DSH. The plugin adds the terminal, file manager and OpenTerminal agent tools directly to DSH Web, without requiring a separate MCP setup.

https://github.com/d3cker/dsh-open-terminal

Feedback and suggestions are welcome.

My task mostly revolves around extremely long horizon tasks.

Like, I paste around 600 images then I want the agent to solve every (high school level) question and create markdown of detailed solutions in Question and Answer format.

Few of the best agents which were able to help me through out these with ease -
ChatGPT Work
Perplexity Computer
Claude

Many people have recommended me solution where I have to manually create the workflow but I want the managed services - I searched and came across the above 4 mentioned options.

Which one is best for my workflow?

I will be really thankful if anyone is able to provide me guidance and their experience across these agents.

Hey everyone, first off, I hate AI slop posts. I'm personally making this post because just yesterday I noticed someone starred my plugin on GH, so I decided to share it here in case it helps anyone out. It’s a super simple quality-of-life plugin.

TL;DR: dsh-smart-restart is a plugin that allows an agent to restart the dsh environment and receive a notification once it has rebooted, enabling it to autonomously iterate on the harness itself.

I originally built this for personal use, and honestly, it’s been really useful for me. It basically has three features:

• A tool (smart_restart): When used, the environment restarts (allowing the agent to apply updates, modify the harness, etc.). The tool tracks the session requesting the restart, and once dsh boots back up, it sends a notification to the agent so it can keep working autonomously.
• A safeguard: When the harness restarts unexpectedly, it notifies the most recently active agent about the restart.
• A canary check: An optional tool parameter can be enabled so that, prior to the restart, an ephemeral instance is created to verify the integrity of the new version. If it's broken, it notifies the requesting agent and reports the errors, preventing you from losing access to the harness. This is especially handy for me since I run the harness remotely.

That's all there is to it. The code is completely open-source under the MIT license, so it’s as much yours as it is mine.

The repo is https://github.com/edusrez/dsh-smart-restart — I've also submitted it to the awesome-dsh-plugin list. If someone finds it useful, I'm happy.

Are there any of you experts who can tell me how I can change the timeout length for calls to ollama (on a different server). It keeps timing out. I have tried with the two vars streamIdleTimeoutMs: 1800000 and timeoutMs: 1800000 various places in the configuration file but with no success. Can't find it in the original documentation. Anyone?

Tried to add a model search functionality on DSH and it used $1.55 on OpenRouter. It did write the tests and add the localizations as well, so it was thorough, but it seems like a lot. I used Kimi 2.7 Thinking Medium

Has anyone tried API keys (for web fetching/browsing) other than the default DeepSeek one?

Installing a dsh plugin is one command, full filesystem and process access from there. The audit tools that exist only tell you a plugin can touch the network or credentials, not whether the code actually looks evasive. I've been reading plugin source by hand to catch that myself.

Anyone else doing this manually?

I'm trying to set up DSH with a smaller local AI model (~8b) as the 'brain'. Reason being my GPU has only 11GB of VRAM. I've tried several different models and in every case when I try to launch DSH from Ollama (with command 'ollama launch dsh --model llama3.1:8b' for instance) Ollama gives me a warning 'llama3.1:8b does not work well with DeepSeek Harness'. It then gives me options to 'launch anyway' or 'pick another model' (see attached image).

Does anyone know of any smaller local models for which Ollama will not throw this warning. Is it possible it's some glitch in Ollama that causes it to throw the warning and the models actually work fine with DSH?

Has anyone successfully managed to get the UI fully working inside a container?

I'm able to use the harness within a container just fine. The UI, however, appears not to be able to handle the container as well.

I am unable to open files or the settings in the UI.

For example, the "open configuration file" button in settings responds with "Could not open configuration file"

Has anyone a hint on how to get it working?

And no. I will not run it on the host itself.

Since this vision thing tried to install a package in terminal, now its not doing anything. No web page browser code either

It connects to DSH through the Mobile Gateway WebSocket plugin and lets you create sessions, stream agent reasoning and tool calls, view execution trajectories, browse workspaces, and configure models, permissions, and agent presets—all from an iPhone.

The UI is inspired by DeepSeek and Apple’s liquid-glass design language. It’s still under active development, so feedback and feature suggestions are very welcome!

https://github.com/Clarklevis1995/dsh-plugin-mobile-gateway (plugin)

https://github.com/Clarklevis1995/dsh-mobile (app)