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jayhe-dsh-gamemaker

DeepSeek Harness plugin: game development role subagents (planner=minimal preset, executor=hard PTC code mode, reviewer=minimal) + bundled game-dev/game-minimal agent presets.

Stars
1
Language
JavaScript
Created
Aug 15, 2026
Updated
Aug 15, 2026

Introduction

jayhe-dsh-gamemaker

Game development role subagents for DeepSeek Harness: a game-director pipeline built from three dedicated subagent providers plus two bundled agent presets.

中文文档

What it provides

Tool / ProviderRoleMode
game-planner-provider (tool game_planner)PlannerComposed from the real minimal-family preset: fixed complete persona, no runtime context, and exactly two tools — persistent shell + str_replace_editor (bash swaps to pwsh on Windows)
game-executor-provider (tool game_executor)ExecutorHard PTC (Code Mode): the child declares presentAs('code') on its own scope, so the model sees only run_code + the generated SDK
game-reviewer-provider (tool game_reviewer)ReviewerSame as planner: the real minimal preset, used read-only

Bundled presets:

  • game-dev (游戏开发工坊) — the conductor: a mandatory pre-work interview (tech route WEB/UE/Unity/Godot, game type, and all other details), the five-phase pipeline, and explicit 【模式切换】 announcements for every mode switch.
  • game-minimal (兼容极简模式) — identical to the shipped minimal preset, plus one special case: on Windows the persistent bash tool is replaced by the deployment's pwsh tool.

How it works

Planner/reviewer children are composed inside the child creation window via ctx.agentPresets.mount(childCtx, 'game-minimal'), which re-parents the child's scope to the preset's standing mount. The child therefore runs the real minimal composition — fixed persona, suppressed runtime context, two tools — instead of a persona + toolFilter approximation. Executor children join the parent preset, then their own scope declares presentAs('code'), making PTC mode hard: direct native calls are rejected, everything goes through run_code programs.

Because a two-tool minimal child cannot call delegation tools itself, the planner authors the dispatch manifest (docs/plan.md — module list with file boundaries, interfaces, acceptance criteria) and the conductor dispatches the executors in parallel, waits with blocking job_output(wait: true) or the system's job-completion notices (no goal-based polling), then hands merging back to the planner and review to the reviewer.

Requirements

  • A dsh deployment with the subagents, agentPresets, and codeRuntime host services (the stock web profile provides all three).
  • Node >= 18 (ESM), bash for the install scripts.

Install

# 1. add the plugin package to the web profile (or link it during development)
export PATH=/usr/local/nodejs22/bin:$PATH        # pnpm may live outside PATH
dsh plugin --profile web add jayhe-dsh-gamemaker
# development:  dsh plugin --profile web add link:/absolute/path/jayhe-dsh-gamemaker

# 2. link the dsh-internal seam packages from the LOCAL dsh installation
bash node_modules/jayhe-dsh-gamemaker/install/link-dsh-deps.sh

# 3. install the bundled presets into the user preset root
bash node_modules/jayhe-dsh-gamemaker/install/install-presets.sh

# 4. add the host row to the profile patch (~/.dsh/profiles/web/cordis.patch.yml)
#    see install/cordis.patch.example.yml

# 5. restart dsh (systemd) and refresh the page
systemctl restart dsh

Then start a new session on the 游戏开发工坊 preset.

Dependency note

The plugin consumes @deepseek-ai/dsh-subagent, @deepseek-ai/dsh-llm, and @deepseek-ai/dsh-session. These packages ship with the dsh deployment itself and are not pinned through npm: the public registry only carries older rc versions with a different API surface. install/link-dsh-deps.sh links the deployment's own copies (auto-detected from the dsh CLI path, overridable via DSH_ROOT), which guarantees API parity with the running harness.

Configuration

The provider plugin accepts one optional field:

- id: game-roles
  name: 'jayhe-dsh-gamemaker'
  config:
    minimalPreset: game-minimal   # preset id composed for planner/reviewer children

License

MIT