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dsh-i-have-adhd

模型推理 更新于 2026.08.26

在终端中运行以下命令:

dsh plugin install yongshuai0314/dsh-i-have-adhd

将以下提示词粘贴到 DeepSeek Harness 对话框中:

在 DeepSeek Harness 中运行 dsh plugin install yongshuai0314/dsh-i-have-adhd 即可安装,源码地址为 https://github.com/yongshuai0314/dsh-i-have-adhd

插件介绍

Out of the box, LLM assistants pad every reply with pleasantries, long-winded context, and a sign-off that adds nothing. For an ADHD brain, that structure buries the one action you actually need to take inside several paragraphs of noise, and the thread is lost before you even reach step two. dsh-i-have-adhd strips all of that away: the first line becomes the command you run, the remaining lines become numbered steps, and the reply ends with a single next move. No openers, no closers, no throat-clearing.

Under the hood it injects one system-prompt section (order 50, after persona, before tool guidance) that carries a ten-rule set into every model step while the mode is on. Three zero-argument agent tools — adhd_on, adhd_off, adhd_status — flip the switch for the current session and persist the choice to a flag file under $DSH_HOME, so the mode survives a restart. Because the section is registered through the systemPrompt service, toggling takes effect on the very next model step: no session restart, no page refresh. The ten rules fall into three groups: Shape (action first, numbered steps, one next move, side quests parked), State (re-anchor where you are, estimate in real units, surface what works now), and Tone (errors are facts, cap lists at five, zero preamble).

It is built for ADHD users who work in DSH and have zero tolerance for verbose replies, and for any developer who would rather get an executable checklist than a readable essay. The plugin does not coach task breakdown, does not change Markdown rendering, does not add rituals. It changes exactly one thing: how the assistant itself writes.

使用场景

  • 调试报错时直接获得可执行步骤而非大段解释
  • 多步骤任务中每步编号清晰并附带时间估算
  • 快速切换会话时回复始终精简无需每次重复要求

适合人员

  • 在 DSH 中编码的 ADHD 开发者
  • 偏好极简可执行清单式回复的工程师
  • 希望减少注意力消耗聚焦下一步行动的技术写作者