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Dyz Med Revision

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duyunzhoudyz
dyz-med-revision

修订已有医学稿件或逐条回应编辑和审稿人意见,覆盖语言、结构、科学表述、改投适配和拒稿后恢复,并保留 可追溯的修改记录。当用户明确要求修改稿件、回复审稿意见或按新期刊重整时使用;先给修订计划,文字和 格式修改可在授权范围内执行,涉及新数据、重分析、伦理、作者或其他源事实时必须先获得确认。 Use for manuscript revision, response to reviewers and journal resubmission preparation.

Overview

Publisherduyunzhoudyz
Repositorydyz-med-paper-skills
Skill namedyz-med-revision
Stars
102
Forks
1
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by duyunzhoudyz on GitHub. Read the source before you install it.

Installation

Install the Dyz Med Revision AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/duyunzhoudyz/dyz-med-paper-skills/tree/main/dyz-med-revision
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/duyunzhoudyz/dyz-med-paper-skills.git /tmp/dyz-med-paper-skills
mkdir -p .claude/skills
cp -r /tmp/dyz-med-paper-skills/dyz-med-revision .claude/skills/dyz-med-revision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dyz Med Revision in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dyz Med Revision on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dyz Med Revision is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

医学稿件修订与审稿回复

入口边界

只做中英互译用 dyz-med-translate;只做语言自然化用 dyz-med-deai;用户尚不清楚稿件问题时先用 dyz-med-manuscript-diagnose。本 Skill 负责实施已明确的修订。

核心原则

  1. 保留原稿,输出新版本或明确的修订副本。
  2. 修改前先说明范围、受保护内容和需要确认的事实。
  3. 不编造数据、分析、伦理、注册、作者贡献、审稿人要求或引用。
  4. 不把审稿人建议自动视为正确;评估后可采纳、部分采纳或有理有据地不采纳。
  5. 每项修改都能追溯到评论、诊断问题、期刊要求或用户目标。

工作流

1. 读取并结构化输入

读取稿件、决定信和审稿意见。保留原始编号与原文,按编辑、Reviewer 1、Reviewer 2 等来源拆分。对文档 解析不完整处记录页码/位置和警告。

2. 分类每条问题

使用:

  • editorial:语言、结构、格式;
  • interpretive:结论强度、解释、适用性;
  • methodological:设计、模型、偏倚控制;
  • data_or_analysis:需要新数据、重分析或新表图;
  • ethics_or_authorship:伦理、注册、署名、利益冲突;
  • journal_adaptation:目标期刊格式和定位。

标记是否可直接处理、需要作者确认或当前无法完成。

3. 先交付修订计划

计划至少包含:

  • 修订模式和目标;
  • 涉及与不涉及的章节;
  • 受保护的数字、事实、引用和声明;
  • 每条问题的处理策略;
  • 需要的新材料或确认;
  • 可能引入的风险;
  • 输出版本和验证方式。

只有真正依赖源事实的事项才暂停;已授权的文字、结构和格式修订可继续执行。

4. 执行最小修改

按章节修改,避免无关重写。科学内容变化必须来自用户提供的结果或实际重新分析输出。引用新增前先核实来源; 引用编号和交叉引用在最终阶段统一更新。

5. 写逐条回复

每条审稿意见包含:

  • 原始评论;
  • 作者回应;
  • 实际修改内容;
  • 准确位置;
  • 回应立场 response_stance:ACCEPT、PARTIAL、DISAGREE_WITH_REASON 或 NEEDS_AUTHOR_INPUT;
  • 独立的问题处理状态:open / in_progress / awaiting_verification / verified_closed;
  • 稳定问题 ID、critical / major / minor 严重度,以及关闭所需的检查与证据。

ACCEPT 表示采纳立场,不证明修改已完成;不同意且理由完整也不自动关闭问题。明确区分已回应、已实施和已验证关闭。 三条均已回复、仅一条完成验证时必须写“回复 3/3,已验证关闭 1/3”,其余两条保持实际未结状态。

语气专业、具体。不同意时解释方法学或证据理由,不攻击审稿人,也不声称做过未完成的分析。

6. 验证受影响内容

重新核对:

  • 数字、表图和摘要一致性;
  • 方法与新分析是否一致;
  • 新增或改写论断的证据;
  • 因果和临床应用边界;
  • 目标期刊要求;
  • 所有审稿意见是否有回应,所有回应是否有对应修改或理由。

按 R2 schema 1.1 将修订输入与产出登记为稳定 artifact ID、path、version、sha256;保留原稿与旧检查。 有 passport 时逐条读取相关记录,无 passport 时可用同形独立记录及明确项目根目录,不强制兄弟技能依赖。 复核记录用唯一 checks[].id、完整 inputs(artifact_id/version/sha256)、scope.domain/description、 execution_status、applicability、domain_result、report:{path,sha256}、checked_at。执行状态为 not_run / blocked / failed / completed,未完成时 domain_result=null;blocked/failed 需 reason。 适用性为 applicable / not_applicable / unknown,后两者需理由。先保存报告,再由独立记录引用哈希。 消费逐条验证输出的 freshness: current / stale / unverified,不能自行存储 current 代替核验。 校验器退出码 0 不是科学 PASS;current 也不保证范围充分或领域结论支持关闭。

只有 verification_check_id 指向 current、completed、applicable 的检查,且审阅其领域结论确实支持解决、 evidence:{path,sha256} 可定位关闭依据时,才可写 verified_closed。部分检查不能关闭未覆盖部分。 结果、稿件或相关依据变化使依赖检查失效,旧关闭不能继续计入当前有效关闭数,应报告需重核并保留历史。 纯排版变更也默认 stale;需沿用科学判断时另建当前输入绑定记录,附等价性和页码/定位重核依据,不改旧哈希。 仅核对受影响范围,不自动串联分析、全面审稿、导出或投稿。

外部审稿/处理前,记录内容类别、用途、具体服务与目的地、允许发送范围和当前人类授权;未知则不发送, 采用已确认本地处理或交付本地待审材料。按 R2 human_approvals 核对 targets 的版本/哈希、role、confirmed_at、 source(human、actor_id、statement、reference、locator)及实际授权权限;recorded_not_authenticated 不证明有效同意。 回执记实际发送字段/工件绑定、目的地、时间、执行状态、结果证据及限制;未发送如实写未发送,不复制敏感正文。 研究参与同意与患者发表同意分开;适用性不明交有资质人员复核,不把伦理批准或去标识化推定为发表豁免。

输出

默认生成:

  • 版本化修订稿;
  • paper/revisions/revision-plan.md;
  • paper/revisions/response-letter.md;
  • paper/revisions/traceability-matrix.md;
  • paper/revisions/open-items.md;
  • 简明变更摘要。

验收标准

  • 原稿可恢复,修订范围清楚;
  • 每条评论有独立的回应立场、处理状态、回应和位置(未修改时写理由,不捏造定位);
  • 所有数字和声明保持或有已确认来源;
  • 未完成分析没有被写成已完成;
  • 分别报告受影响章节的复核结果、范围与 freshness;未通过、未核实或待作者输入时只交付标明限制的修订草稿,不声称全部关闭。

配套资源

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dyz Med Revision AI skill do?

修订已有医学稿件或逐条回应编辑和审稿人意见,覆盖语言、结构、科学表述、改投适配和拒稿后恢复,并保留 可追溯的修改记录。当用户明确要求修改稿件、回复审稿意见或按新期刊重整时使用;先给修订计划,文字和 格式修改可在授权范围内执行,涉及新数据、重分析、伦理、作者或其他源事实时必须先获得确认。 Use for manuscript revision, response to reviewers and journal resubmission preparation.

Why use Dyz Med Revision on TypingMind?

Because you install it once and use it with any model. Dyz Med Revision is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dyz Med Revision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/duyunzhoudyz/dyz-med-paper-skills/tree/main/dyz-med-revision. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dyz Med Revision?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dyz Med Revision?

As many as you like. As long as a model supports skills, you can use Dyz Med Revision with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dyz Med Revision AI skill free?

It is published on GitHub by duyunzhoudyz. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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