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dsh-skill-router

Workflow Updated 2026.08.17

Run the following command in DeepSeek Harness:

dsh plugin install MJorgin/dsh-skill-router

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install MJorgin/dsh-skill-router in your terminal to install the plugin. The full source is at https://github.com/MJorgin/dsh-skill-router . Restart your DeepSeek Harness instance after installation to activate it.

About this plugin

Most skill loading still relies on the model to decide every step, which often means late loads, wrong picks, or missed opportunities entirely. dsh-skill-router steps in before the model starts thinking: it runs deterministic YAML rules against the latest user message, pours matched skill bodies into the current step on a hit, and stays completely silent on a miss. Zero LLM calls, zero token cost, zero added latency, and each skill pours at most once per session. A broken policy file simply falls back to bundled defaults without breaking the session.

The design philosophy is simple: rules are the policy, the plugin is the execution. Add a URL-path rule and a Feishu document routes to the right skill in microseconds. Write a short trigger phrase in a skill's whenToUse field and it becomes a secondary match. No embeddings, no model judge, fully reproducible every time. It complements skill-bartender's judgment layer or works perfectly standalone.

Best for developers who already maintain a growing skill catalog in DeepSeek Harness and want loads to be faster, more accurate, and token-free. If you prefer tuning routing by editing data rather than writing code, this YAML file is your only entry point.

Screenshots

Use Cases

  • A URL-path rule hits and the corresponding document skill pours in before the model responds
  • Repeated triggers of the same skill are deduplicated so context never floods with duplicates
  • A malformed YAML policy silently falls back to bundled defaults without breaking the session

Best For

  • Developers with a growing skill catalog in DeepSeek Harness who want precise, deterministic loads
  • Workflow builders who demand zero LLM calls, zero token cost, and full reproducibility for skill routing
  • Engineers who prefer tuning routing by editing YAML data over writing or modifying code