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dsh-chess-pick

Fun & Dress-up Updated 2026.08.21

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-chess-pick

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

Run dsh plugin install uckkk/dsh-chess-pick in your DeepSeek Harness terminal to install this plugin; the full source code is available at https://github.com/uckkk/dsh-chess-pick .

About this plugin

You want to learn chess but are frozen by the sheer number of options on the table: international chess, xiangqi, go, shogi. Each one has different rules, different pacing, and different learning curves. dsh-chess-pick was built for exactly that moment of indecision. It registers a single tool called chess_pick; you feed it your preferences and constraints, and it hands back a tailored beginner study plan that says which game to start with and how to structure your early practice.

The whole plugin is plain Node.js with zero network dependencies. There are no third-party runtime packages to resolve, no remote API calls to worry about. Install it, call the tool, get a result. That makes it practical in offline sandboxes, CI pipelines, or any environment where a Node runtime is all you have.

It is a good fit for the curious newcomer who has wanted to start for months but never picked a board, and for organizers who need a quick, defensible chess-learning path for a club, a classroom, or a family evening. You supply the parameters; it supplies the plan; you break the inertia and sit down to play.

Use Cases

  • A beginner curious about board games wants a quick recommendation and a first-month path
  • Planning a chess-learning sequence for a club or a parent-child class
  • Generating a study plan inside an offline sandbox or CI pipeline
  • Comparing rule sets of chess, go, and shogi to pick the best starting point

Best For

  • Curious newcomers who have wanted to start playing but never chose a game
  • Instructors and organizers who need a fast, defensible chess-learning path
  • Node developers who prefer local, zero-network tooling over cloud services