review-write

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SUMMARY

Evidence-preserving professional writing review and rewrite Skill for Chinese and English: review first, protect facts, deliver clean body.

README.md

审写 · ReviewWrite

把不该交付的 AI 过程残留,变成可核验的专业正文。

Review before rewrite. Keep the facts. Lose the slop.

不是 AI 检测规避器;是专业写作的交付质量控制。

可复现证据:4 份合成案例的严格预检从 14 fail / 23 warn 降至 0 / 0;38 项受保护字面量通过逐字回归检查。查看完整报告

中文 · English

审写 · ReviewWrite is an evidence-preserving review-and-rewrite Skill for professional writing. It provides compatibility and installation guidance for Codex, Claude Code, Hermes Agent, Gemini CLI, GitHub Copilot, OpenClaw, WorkBuddy, and other Agent Skills-compatible systems.


中文

先审后写,保护事实,交付正文

审写 · ReviewWrite 面向中英文专业写作:先判断文本和目标,再评审、改写、复核。它保护事实、引用、限定条件和作者声音,不把“像人”理解为制造错误或规避检测。

名称与调用:短,但不混乱

  • 技术 Skill ID:reviewwrite。支持 slash 调用的平台使用 /reviewwrite
  • 产品名:审写 · ReviewWrite。
  • 自然语言简称:审写、ReviewWrite。它们只用于支持自然语言触发的宿主,不是第二份可安装 Skill,也不保证成为 slash alias。
  • 不要并行安装旧 review-write 和新 reviewwrite;安装器发现旧目录时会拒绝并行安装,先报告路径,再由用户决定迁移或删除。

想了解不同模型常见 AI 味、普通 humanizer 的风险,以及如何把“去 AI 痕迹”前移到写作契约,见 AI 写作痕迹与 Humanizer 策略

一句话交给智能体安装

复制下面代码块中的内容:

请从官方仓库 https://github.com/songhai-dg/review-write 安装 ReviewWrite Skill;如当前网络无法访问 GitHub,使用同步镜像 https://gitee.com/cufe01/songhai-dg。如果当前智能体已经安装,不要重复安装,也不要搜索或替换成名称相近的其他技能。请使用所在平台自己的 Skill 安装或导入机制;如果仓库已在当前目录,直接以当前目录为安装来源。平台不支持、没有权限或无法访问来源时,说明限制并停止。

国内 SkillHub 入口:审写 · ReviewWrite

正式版本会自动生成一份分发包,统一提供 GitHub、Gitee、SkillHub 和其他 Agent Skills 目录所需的安装指令、版本信息与宣传文案,避免各平台出现不同版本。分发包说明见 distribution/README.md

它主要解决:

  • 提示词、推理过程、工具调用和编辑指令混入正式正文;
  • 表面“去 AI 腔”导致事实、数字、引用、限定条件和作者声音漂移;
  • 用一套通用规则误改论文、基金、公众号、政策、公文等不同体裁;
  • 把中文规则直接翻译到英文,或忽略本语种、地区和机构的表达规范。

核心闭环

Understand → Review → Plan → Rewrite → Verify → Evolve
理解文本      独立评审    约束修改     生成正文      双重复核      保留经验

ReviewWrite 严格分离四个写作表面:评审报告、修改计划、正式正文和复核报告。只有正式正文可以成为对外交付文本。

核心能力

  • 适配论文、基金、政策、报告、公文、备忘录、营销和双语写作;
  • 根据语言、读者和真实体裁选择表达,不强套模板;
  • 增加技术解读/产业评论路由:检查模型、推理、设备、内存和性能主张中的叠加式模板信号与技术口径;
  • 默认保护数字、引用、专名、义务和限定条件;
  • 将评审、修改稿和复核分开,并对正式正文做泄漏预检;
  • 泄漏预检支持体裁与语境感知:AI 安全论文讨论 system prompt、公文使用结构化条目等被授权情形,用 --context/--genre 声明后降级或放宽,避免正当写作被误判;
  • 识别空泛强化和公式化转折,但必须按体裁、上下文与证据判断,不使用生硬禁词表;
  • 识别技术评论中的叠加式模板信号,以及公众号文章中反复使用“第一、第二、第三”的机械枚举。
  • 对 DOCX/PPTX 提供可选的只读字体与渲染质检:检查中英文字体声明、主题/继承不确定项、profile 不匹配和目标字体库存;不静默改文件。

AI 风格与过程残留审查清单

下表列的是可观察的文本信号和交付风险,不是“AI 生成概率”,也不据此判断作者身份。单个词语通常不会触发处理;技能结合体裁、重复频率、证据和段落功能判断。

类型 / 规则 识别范围 常见表现 处置与不误伤边界
提示词泄漏 RW-F-001 system/developer prompt、角色标签、忽略指令等 “系统提示词”“忽略以上要求”“[INST]” 正文 blocker;AI 安全论文等授权语境降为人工复核警告
隐藏推理泄漏 RW-F-002 思考链、分析通道、逐步内部推理 “以下是我的完整思考过程” 正文 blocker;不得把内部推理写进交付正文
模型自述 RW-F-003 AI/语言模型身份和能力免责声明 “作为一个 AI……” 正文 blocker;合法的 AI 辅助披露可声明语境后复核
任务与编辑残留 RW-F-004/RW-W-102 用户要求、改稿待办、版本过程 “根据用户要求”“待补充”“下一轮修改” 任务复述和编辑指令不得进入正文;评审报告可以引用
工具/运行时残留 RW-F-005 工具调用、Skill、Agent、内部路径和路由 “我调用了工具”“SKILL.md”“working tree” 正文 blocker;软件文档或审计报告需声明讨论语境
工作流与聊天收尾 RW-W-101/RW-W-103 过程预告、助手交接语 “下面我将介绍”“希望这对你有帮助” warn;只清理正文中的过程话术,不改真实对话转写
空泛开场与重要性 RW-W-201/RW-W-202/RW-W-207 时代背景、无标准的“重要/关键/全面提升” “在当今时代”“这件事意义重大” 改成主体、动作、对象、范围和依据;正式规范词保留
公式化转折与悬念 RW-W-203/RW-W-208 二元反转、悬念桥接和重复总结 “不是……而是……”“换句话说”“最危险的地方” 单次有逻辑可保留;连续堆叠或不增加信息时 warn
宣传腔与格式模板 RW-W-204/RW-W-205 空泛赋能、固定卖点、标签式列表 “全面赋能”“打造新生态” 结合政策、营销和公文体裁判断,不使用跨体裁禁词
技术评论叠加信号 RW-W-209—RW-W-212 模型/推理/设备文本中的反转、重要性、用户泛化、预测和数字 “这个细节很关键”+“大多数用户”+“更可能” 只在 public-article/technical-commentary 语境告警;要求补机制、指标、来源和条件
机械枚举 RW-W-213 三个以上句首“第一、第二、第三/首先、其次、最后” 全文每段都用同一排序节奏 warn;先区分事实、证据、判断、行动;政策条文、方法步骤和正式清单可保留

覆盖范围:中文 zh-CN 与通用专业英语;论文、基金、公众号、技术解读、政策、公文、报告、备忘录、营销和双语文本。规则输出评审信号,不生成“人味分数”,也不承诺规避任何检测器。

四个可复现案例

四份合成案例直接展示问题稿、修改稿和验证结果:严格预检由 14 个硬失败 / 23 个警告 降至 0 / 0;38 项受保护字面量(数字、日期、角色与限定词)通过逐字回归检查。查看完整复现报告

案例 问题稿 修改稿 主要改进 预检
学术摘要 查看 查看 去除模型身份和推理泄漏,保留样本边界 5 fail / 4 warn → 0 / 0
基金申请 查看 查看 删除宣传腔和待办,补清研究设计 3 fail / 6 warn → 0 / 0
政策简报 查看 查看 清理工具残留,明确选项和证据缺口 2 fail / 8 warn → 0 / 0
英文管理备忘录 查看 查看 直接呈现决策、约束和风险 4 fail / 5 warn → 0 / 0

安全与来源

发布包只包含 Skill、参考资料、一个配置结构样例和本地文本/Office QA 脚本;它们不联网、不读取凭证、不修改或上传输入正文与 Office 原件。Office QA 只有在显式要求渲染时才向预览目录写入 PDF/PNG。安装前请核对官方 Release、SHA-256 与 artifact attestation,并在已有安装时拒绝覆盖未知目录。完整边界见 SECURITY.md

Office 交付前质检:先审计,再决定是否修复

当用户交付 Word 或 PPT 时,ReviewWrite 可以在正文审写之后增加一个可选的 Office QA 环节。它不是“自动统一字体”:单位模板优先,默认只读审计,原件不会被覆盖。

# 默认只读结构审计;不生成渲染预览
python3 scripts/office_qa.py path/to/submission.docx --format json

# 模板已确认时再对照字体 profile,并要求完成渲染
python3 scripts/office_qa.py path/to/deck.pptx \
  --font-profile examples/office-qa/font-profile.example.json \
  --render required --output-dir /tmp/reviewwrite-preview --format json

报告会区分“字体缺失/回退风险”和“字符本身损坏”,不会把后者伪装成字体问题。结构审计或生成 PDF/PNG 预览都不等于视觉通过:必须逐页查看缺字、字体回退、截断、溢出和异常换行。没有确认模板、目标字体库存或视觉检查时,Skill 不会声称文件在所有设备上均可正常显示。详见 Office QA 说明字体 profile 规范

使用示例

使用 ReviewWrite 评审并修改这份政策简报。先给问题清单,再给修改稿,不能改变数字和政策口径。
使用 ReviewWrite 修改这篇英文论文摘要。保留术语、样本量、统计结果和引用,不要把编辑过程写进摘要。

多语言,不只是翻译

当前核心支持 zh-CN 和通用专业英语。语言能力使用三个坐标:语言、地区规范和话语共同体。英文不能默认等于美式商业写作,中文也不能默认增加政策口号、谦辞或成语。

新增语言必须经过独立语言包、locale-aware few-shot、事实保持回归和本语种专业 review,才能从 planned 进入 experimental,再进入 core。详见语言包规范

few-shot 如何设计

few-shot 不是越大越好的模板库。每次最多选择三个:一个修复最高风险问题,一个校准体裁和 locale,必要时一个保持作者声音。授权、语言、体裁或文化语境匹配不足时宁可不用。示例只传递修改原则,不迁移事实、观点、专名或标志性句式。详见 few-shot policy

检查与测试

python3 scripts/reviewwrite_lint.py path/to/draft.md
python3 scripts/reviewwrite_lint.py path/to/draft.md --strict
python3 scripts/validate_skill.py
python3 -m unittest discover -s tests -v
python3 scripts/package_skill.py

更新与发布

ReviewWrite 默认只提示更新,不在活跃写作任务中静默替换 Skill。联网检查最多每 24 小时一次;宿主平台已有原生版本管理时优先使用平台渠道。

版本采用双轨制:有证据的小改进进入 edge patch;通过完整回归和人工 review 后提升为 stable minor;没有有效变化就不发布。详见运营策略更新策略

站在已有项目之上

ReviewWrite 吸收并重新组织 Stop Slop、Humanizer-zh 和 skill-deslop 的可复用能力,同时增加文档本体、体裁与语言路由、泄漏硬门、few-shot 选择和改后验证。精确来源、版本和许可见 NOTICE.mdsources.lock.json

贡献与许可

欢迎提交体裁包、语言包、授权 few-shot、误报样例和事实保持回归。请先阅读 CONTRIBUTING.md。项目采用 MIT License。


English

Review first, then rewrite

审写 · ReviewWrite is built for professional Chinese and English writing: it identifies the goal and context, then reviews, rewrites, and verifies the text while preserving facts, citations, qualifications, and authorial voice. It does not promise detector evasion or add mistakes to imitate a human author.

Naming and invocation: short without ambiguity

  • Technical Skill ID: reviewwrite. Use /reviewwrite on hosts that support slash invocation.
  • Product name: 审写 · ReviewWrite.
  • Natural-language names: 审写 and ReviewWrite. They are only natural-language triggers on hosts that support them, not separately installable Skills or guaranteed slash aliases.
  • Do not install both legacy review-write and reviewwrite. The installer refuses a parallel install when it detects the old directory, then reports its path for a user-directed migration or removal.

For AI writing signals, common humanizer risks, and ReviewWrite's writing-before-rewriting strategy, see AI writing signals and humanizer strategy.

One-sentence agent installation

Copy the text in the code block:

Install ReviewWrite from the canonical repository https://github.com/songhai-dg/review-write. If GitHub is unavailable on the current network, use the synchronized Gitee mirror https://gitee.com/cufe01/songhai-dg. If the current agent already has it, do not install it again or search for a similarly named replacement. Use the current platform's native Skill installation or import flow; if the repository is already in the current directory, use that directory as the installation source. If the platform is unsupported, permissions are unavailable, or the source cannot be reached, state the limitation and stop.

It is not an AI-detector bypass tool; it is delivery-quality control for professional writing.

It addresses four recurring failures:

  • prompts, reasoning, tool calls, or editorial instructions leaking into the deliverable;
  • surface-level “humanization” changing facts, numbers, citations, qualifications, or authorial voice;
  • one generic style rule being applied across papers, grants, public articles, policies, and official documents;
  • translating rules between languages without respecting locale, discourse community, or institutional convention.

Core loop

Understand → Review → Plan → Rewrite → Verify → Evolve

ReviewWrite separates four writing surfaces: the review report, revision plan, deliverable body, and verification report. Only the deliverable body belongs in the publication-ready text.

Core capabilities

  • genre-aware Chinese and English writing support;
  • technical commentary review for model, inference, device, memory, and performance claims, including composite template signals and scope checks;
  • context-sensitive routing by language, audience, locale, and discourse community;
  • protection for facts, numbers, citations, names, obligations, and qualifications;
  • separated review, revision, deliverable, and verification surfaces with leakage checks;
  • genre- and context-aware preflight: authorized cases such as an AI-safety paper discussing system prompt, or an official document using structured items, are declared with --context/--genre and downgraded or relaxed instead of hard-failing legitimate writing;
  • contextual checks for empty intensifiers and formulaic pivots, rather than a rigid blacklist of “AI words”;
  • composite checks for stacked technical-commentary signals and repetitive sentence-initial enumeration, with genre-aware exemptions.
  • optional audit-only DOCX/PPTX delivery QA for Chinese/Latin font declarations, theme/inheritance uncertainty, confirmed-profile mismatches, target font inventories, and a render gate; it never silently rewrites a source Office file.

AI-style and process-residue coverage

These are observable writing and delivery signals, not an authorship classifier. A single word is rarely enough; ReviewWrite combines genre, recurrence, evidence, and paragraph function.

Type / rules Scope Typical signal Handling and boundary
Prompt leakage RW-F-001 System/developer prompts, role tags, instruction overrides system prompt, [INST], “ignore previous instructions” blocker in deliverables; authorized AI-safety discussion is downgraded for review
Hidden reasoning RW-F-002 Chain-of-thought, analysis channels, internal step-by-step reasoning “my complete reasoning process” blocker; never place hidden reasoning in the deliverable
Model self-description RW-F-003 AI identity and capability disclaimers “As an AI…” blocker; authorized disclosure is reviewed in context
Task/editor residue RW-F-004/RW-W-102 User-task recap, TODOs, revision workflow “based on the user request”, “TODO” Remove from deliverables; review reports may quote the span
Tool/runtime residue RW-F-005 Tool calls, Skill/Agent names, internal paths and routes tool call, SKILL.md, working tree blocker in deliverables; software docs/audits declare their context
Workflow/chat handoff RW-W-101/RW-W-103 Process previews and assistant closings “This section will…”, “Hope this helps” warn; does not rewrite genuine dialogue transcripts
Empty opening/significance RW-W-201/RW-W-202/RW-W-207 Generic era framing and unsupported emphasis “in today’s world”, “highly significant” Replace with actor, action, scope, standard, and evidence; preserve formal normative terms
Formulaic contrast/bridge RW-W-203/RW-W-208 Binary reversals, suspense bridges, repeated summaries “not X but Y”, “in other words”, “the most dangerous part” One functional contrast may stay; stacked or empty uses warn
Promotional/format template RW-W-204/RW-W-205 Empty enablement language, fixed selling points, label lists “unlock the ecosystem”, bold label blocks Apply genre-aware review; no cross-genre blacklist
Technical stacked signals RW-W-209—RW-W-212 Model/inference/device prose combining contrasts, importance, audience generalization, forecasts, and numbers “the key detail” + “most users” + “more likely” Warn only in public-article/technical-commentary; request mechanism, metrics, sources, and conditions
Mechanical enumeration RW-W-213 Three or more sentence-initial ordinal markers Every paragraph starts “First, Second, Third” warn; distinguish facts, evidence, judgment, and action; retain policy clauses and method steps

Coverage includes zh-CN and general professional English across papers, grants, public articles, technical commentary, policy, official documents, reports, memos, marketing, and bilingual writing. Findings are review signals, not a “human score” or detector-evasion promise.

Four reproducible cases

Four synthetic cases show the draft, revision, and verification result. Strict preflight moved from 14 hard failures / 23 warnings to 0 / 0; a literal regression check retained 38 protected items (numbers, dates, roles, and qualifiers). Read the full reproducibility report.

Case Draft Revision Main improvement Preflight
Academic abstract View View removes model identity and reasoning leakage 5 fail / 4 warn → 0 / 0
Grant rationale View View removes promotional language and TODOs 3 fail / 6 warn → 0 / 0
Policy brief View View clarifies options and evidence gaps 2 fail / 8 warn → 0 / 0
Executive memo View View puts decision, constraints, and risks first 4 fail / 5 warn → 0 / 0

Security and provenance

The release bundle contains only the Skill, references, one configuration-structure example, and local text/Office QA scripts. They do not make network requests, read credentials, modify or upload input prose or source Office files. Only an explicitly requested Office render writes PDF/PNG previews to an output directory. Verify the official Release, SHA-256, and artifact attestation before installation, and never overwrite an unknown existing directory. See SECURITY.md for the full boundary.

Office delivery QA: audit before any repair

When a Word document or PowerPoint deck is part of the deliverable, ReviewWrite can add an optional Office QA pass after text review. It is not an “apply one font everywhere” button: the confirmed institutional template wins, the default is read-only, and the source file is never overwritten.

# Default read-only structural audit; it does not generate a render preview.
python3 scripts/office_qa.py path/to/submission.docx --format json

# Use a confirmed profile and require that a render can be produced.
python3 scripts/office_qa.py path/to/deck.pptx \
  --font-profile examples/office-qa/font-profile.example.json \
  --render required --output-dir /tmp/reviewwrite-preview --format json

The report separates font-availability/fallback risk from damaged characters. A structural pass or generated PDF/PNG is not visual approval: inspect every page or slide for missing glyphs, fallback, clipping, overflow, and unexpected wrapping. Without a confirmed template, target font inventory, and visual inspection, ReviewWrite does not claim that a file will display correctly on every recipient device. See Office QA and the font-profile specification.

Usage examples

Use ReviewWrite to review and revise this policy brief. List the issues first, then provide the revision. Preserve every number and policy qualification.
Use ReviewWrite to revise this academic abstract. Preserve terminology, sample size, statistical results, citations, and the author's English variant. Do not leak editorial process into the abstract.

Multilingual by design

The current core supports zh-CN and general professional English. Language behavior is modeled through three coordinates: language, locale, and discourse community. English is not assumed to mean US business prose, and Chinese is not assumed to require slogans, honorific padding, or idioms.

A new language must pass a dedicated language pack, locale-aware few-shots, fact-preservation regressions, and professional native-language review before moving from planned to experimental and then core. See the language-pack specification.

Few-shot design

Few-shots are not a growing template dump. A task may select up to three examples: one for its highest-risk failure, one for genre and locale, and, when authorized, one for author voice. If authorization, language, genre, or cultural context does not match, ReviewWrite uses no example. Examples teach transformations; they do not transfer facts, opinions, names, or signature phrasing. See the few-shot policy.

Validation and tests

python3 scripts/reviewwrite_lint.py path/to/draft.md
python3 scripts/reviewwrite_lint.py path/to/draft.md --strict
python3 scripts/validate_skill.py
python3 -m unittest discover -s tests -v
python3 scripts/package_skill.py

Updates and release cadence

ReviewWrite notifies by default and never replaces a Skill during an active writing task. Network checks are cached for up to 24 hours. A host platform's native update channel takes precedence when available.

Qualified small improvements enter the edge channel as patches. A stable minor is promoted only after full regressions and human review. No meaningful improvement means no release. See operations and the update policy.

Built on existing work

ReviewWrite learns from and reorganizes reusable ideas from Stop Slop, Humanizer-zh, and skill-deslop. It adds a writing ontology, genre and language routing, a delivery-leakage gate, few-shot selection, and post-rewrite verification. Exact sources, revisions, and licenses are recorded in NOTICE.md and sources.lock.json.

Contributing and license

Contributions of genre packs, language packs, authorized few-shots, false-positive examples, and fact-preservation regressions are welcome. Read CONTRIBUTING.md first. ReviewWrite is released under the MIT License.

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