Agent skill

Researchwrite Proposal Pipeline

by Yuan1z0825 in Yuan1z0825/nature-skills

Composes, revises or audits research proposals and opening reports through an evidence-first state machine with argument maps, section contracts and dynamic expert reviewers.

MITAuto-check passedWriting & Content

SKILL.md written in Chinese; this summary is our English description.

Install Researchwrite Proposal Pipeline

skills CLI
$ npx skills add Yuan1z0825/nature-skills --skill researchwrite -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yuan1z0825/nature-skills researchwrite --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yuan1z0825/nature-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-proposal-writer .claude/skills/researchwrite && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
researchwrite
GitHub stars
47k
Used in
1 other repo
Token cost
~1.1k tokens
SKILL.md length
213 words
Files
36 (incl. scripts, references)
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

Composes, revises or audits research proposals and opening reports through an evidence-first state machine with argument maps, section contracts and dynamic expert reviewers.

  • Works in 9 steps: 证据先于文字 — 起草前必须建立或读取 research_canon 和… → 论证先于章节 — 写正文前必须完成 argument_map → 契约先于段落 — 每节需要 purpose / allowed claims /… → …
  • Drafting a Chinese research proposal or opening report from a topic
  • SKILL.md covers 核心原则, 模式分派, 项目结构 and Reference 文件索引, plus 3 more sections
  • Revising an existing proposal against an evidence table

What it does

This is a research-writing state machine, not a generic prompt to write a paper, inspired by projects for state-and-score workflows, dynamic experts, entry questions and AI-writing cleanup. Nine principles govern it: evidence before text, with a research canon and evidence table built first; an argument map before sections; a contract for every section listing purpose, allowed claims, forbidden claims, inputs and validation; scope before completeness; experts summoned by failure mode; content before language polish; no upgrading hedged claims into proof; deletion over explanation; and stopping on a plateau, expert conflict or missing evidence.

Input decides the mode: compose for a topic or vague idea, revise for existing sections or a full proposal, and hybrid for a draft that needs expansion or restructuring. Work lives in a project folder under `researchwrite/` with standard files such as scope, research canon and evidence table, and `manifest.yaml` routes templates and loads references only when needed. References cover an eight-dimension scoring rubric, Chinese anti-filler cleanup, review-writing style, stopping rules, professor dispatch, reference renumbering, validation, export to Markdown or Word and handoff briefs.

When your agent uses it

  • Drafting a Chinese research proposal or opening report from a topic
  • Revising an existing proposal against an evidence table
  • Auditing a proposal for unsupported claims and scope creep
  • Exporting a finished proposal project to Markdown or Word

Example prompts

  • “Compose a research proposal on solid-state electrolytes starting from my topic and notes.”
  • “Revise chapter 2 of my opening report and flag any claim the evidence table does not support.”
  • “Audit my proposal draft and tell me whether to keep iterating or stop.”
  • “Export the finished proposal project as a .docx file.”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. 证据先于文字 — 起草前必须建立或读取 research_canon 和 evidence_table
  2. 论证先于章节 — 写正文前必须完成 argument_map
  3. 契约先于段落 — 每节需要 purpose / allowed claims / forbidden claims / inputs / validation
  4. 范围先于完备 — 如果是分阶段写作,先锁定阶段边界
  5. 动态专家,不设固定池 — 用 professor 按失败模式召唤对应专家
  6. 内容先于语言 — 诊断科学逻辑后再做 anti-slop / 语言打磨
  7. 不自动升级事实 — 永远不把 "may indicate" 改成 "proves",除非有证据支撑
  8. 删除胜于解释 — 当某主张不可行,直接删除。正文干净,解释留给答辩
  9. 该停就停 — 平台期、专家冲突、证据缺失是停止理由,不是润色理由

What it can do on your machine

Read from SKILL.md and the folder at commit 2a20e4a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Researchwrite Proposal Pipeline loads about 1.1k tokens when it runs, and up to ~20k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 213 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~20k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Yuan1z0825/nature-skills at commit 2a20e4a, republished under its MIT licence (© Yuan1z0825). 213 words, ~1,122 tokens.

Download SKILL.mdSave it as .claude/skills/researchwrite/SKILL.md (or your agent's skills folder). This skill also uses 35 other files; get the full folder from GitHub.
name
researchwrite
description
Compose, revise, or audit research proposals, opening reports, and research plans from supporting evidence. Use for 研究计划、开题报告、科研项目申请书. Invoked as researchwrite for compatibility; use nature-writing for general manuscript sections.
license
MIT

researchwrite — proposal-first 科研写作 pipeline

受 autonovel(状态机+打分)、professor(动态专家)、brainstorming(入口追问)、anti-AI-writing(语言清理)启发的科研写作状态机。不是通用"帮我写论文"prompt。

researchwrite 是为兼容既有安装保留的触发名称,仓库目录名仍为 nature-proposal-writer。不要在没有迁移方案时修改 frontmatter 名称。需要初始化模板、 按条件加载参考资料或执行导出脚本时,先读取 manifest.yaml,只加载与当前条件匹配的路径。

核心原则

  1. 证据先于文字 — 起草前必须建立或读取 research_canon 和 evidence_table
  2. 论证先于章节 — 写正文前必须完成 argument_map
  3. 契约先于段落 — 每节需要 purpose / allowed claims / forbidden claims / inputs / validation
  4. 范围先于完备 — 如果是分阶段写作,先锁定阶段边界
  5. 动态专家,不设固定池 — 用 professor 按失败模式召唤对应专家
  6. 内容先于语言 — 诊断科学逻辑后再做 anti-slop / 语言打磨
  7. 不自动升级事实 — 永远不把 "may indicate" 改成 "proves",除非有证据支撑
  8. 删除胜于解释 — 当某主张不可行,直接删除。正文干净,解释留给答辩
  9. 该停就停 — 平台期、专家冲突、证据缺失是停止理由,不是润色理由

模式分派

输入模式
题目、方向、模糊想法compose — 加载 references/compose-mode.md
已有段落/章节/完整 proposalrevise — 加载 references/revise-mode.md
已有草稿 + 扩写/补写/重构hybrid — 加载 references/hybrid-mode.md

模糊时推断默认模式。只在选择会改变工作流时才问用户。

项目结构

工作目录:<outputs>/researchwrite/<project-slug>/

标准文件:

00_scope.md              写作任务边界
01_research_canon.md     硬事实和约束
02_evidence_table.md      claim → evidence 映射表
03_argument_map.md        论证架构
04_section_contracts.md   每节的 purpose / inputs / allowed & forbidden claims
05_style_guide.md         风格、术语、禁用表达
state.json                项目状态(mode、round、scores、technical_debts)
sources/                  用户材料、文献、数据
drafts/                   草稿和分节文件
revision_briefs/          修订简报
qa_logs/                  诊断、专家审查、anti-slop、打分记录
exports/                  最终输出(.md + .docx)

新建项目时按 manifest.yaml 的 templates.on_demand 路由从 templates/ 取空模板。 references/worked-example-quaternary-proposal.md 提供了基于通用材料科学领域的完整填写示例。

Reference 文件索引

完整 reference 按任务需要加载,不要一次性注入全部文件:

Reference加载时机
references/compose-mode.md模式 = compose(9 步流程)
references/revise-mode.md模式 = revise(9 步流程)
references/hybrid-mode.md模式 = hybrid
references/evaluation-rubric.md使用 8 维 × 4 锚点评分体系时
references/research-anti-slop.md清理中文 proposal 的模板化和空泛表达时
references/chinese-review-writing-style.md撰写或修订中文综述时
references/stopping-rules.md判断继续迭代、拆分范围或停止时
references/professor-dispatch.md按失败模式分派动态专家时
references/foundation-files.md建立或修复 foundation 五文件时
references/project-structure.md初始化项目目录或维护 state.json 时
references/export-archive.md导出和归档 .md / .docx 时
references/partial-proposal-scope.md分阶段写作并防止范围蔓延时
references/ref-renumbering-cascade.md处理参考文献增删和编号级联时
references/review-paper-framework.md设计综述论文框架时
references/review-critique-methodology.md建立综述的批判性分析时
references/validation-checklist.md自动检查主张、引用、编号和可复现性时
references/gpt-handoff-revision-brief.md生成跨 agent 修订交接简报时
references/within-approved-proposal.md在已批准的 proposal 框架内扩写时
references/worked-example-quaternary-proposal.md需要查看完整 foundation 文件填写示例时
references/降承诺提案模式.md证据不足,需要降低承诺强度时

运行交付

每次运行结束时输出:

  1. 当前文件路径或修订后文本
  2. 当前分数/状态
  3. 剩余风险
  4. 一条建议的下一步

QA Mode — 四层质量保障 Pipeline

当用户说"审查这段/跑 QA/检查方案/过 pipeline"时,进入 QA 模式。

情境挡位
挡位适用场景阈值
paper投稿论文7.0
proposal研究方案/开题7.0
internal内部汇报/周报5.0
quick快速扫读无
Pipeline 顺序(先内容后语言)
Gate 2: professor Convener(内容层)
  ├── 论文 → 方法论专家 + 领域专家
  ├── proposal → 可行性专家 + 创新性专家
  └── 文献综述 → 覆盖面专家 + 批判深度专家
  │
  ▼
Gate 1: avoid-ai-writing 模式 detect-only
  └── 只对英文有效,中文跳过或降级为手工扫读
  │
  ▼
Gate 3: auto-validation(格式/完整性层)
  └── 每个 claim 有 citation?方法可复现?编号连续?
  │
  ▼
Gate 4: 评分阈值(分维度打分)
  ├── 总分≥阈值 → 通过 ✅
  └── 总分<阈值 → 按低分维度定向回退(不超过 3 轮)

Gate 2 在 Gate 1 之前 — 避免改了句子后被专家打回重写。

使用方法
用户: "用 researchwrite 审查这段 discussion,paper 挡位"
  → 自动走 Gate 2 → Gate 1 → Gate 3 → Gate 4
  → 返回审查报告 + 定向修改建议

用户: "快速扫一下这个邮件"
  → 走 quick 挡位,只标记 P0 问题

配置你的研究域

首次使用告诉 agent 你的研究背景,agent 会调用 professor 建立领域专家知识。后续写作中专家审查会基于你的领域。

© Yuan1z0825, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 35 other files (scripts, references) in skills/nature-proposal-writer of Yuan1z0825/nature-skills.

  • SKILL.md
  • README.md
  • README_EN.md
  • agents/openai.yaml
  • manifest.yaml
  • references/chinese-review-writing-style.md
  • references/compose-mode.md
  • references/evaluation-rubric.md
  • references/export-archive.md
  • references/foundation-files.md
  • references/gpt-handoff-revision-brief.md
  • references/hybrid-mode.md
  • references/partial-proposal-scope.md
  • references/professor-dispatch.md
  • references/project-structure.md
  • references/ref-renumbering-cascade.md
  • references/research-anti-slop.md
  • references/review-critique-methodology.md
  • references/review-paper-framework.md
  • … and 17 more

Open the folder on GitHubat commit 2a20e4a

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Yuan1z0825/nature-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Academic HumanizerAIScientists-Dev/academic-humanizer1.9k1 repos~4.2kAutomated safety check: PassMIT
NSFC Abstract Writerhuangwb8/ChineseResearchLaTeX2.9k1 repos~1.3kAutomated safety check: PassMIT
NSFC Research Significance WriterHuiyuLi-2000/Chinese-Grant-Writer-Skills4341 repos~790Automated safety check: PassMIT

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Questions about Researchwrite Proposal Pipeline

What does Researchwrite Proposal Pipeline do?

Composes, revises or audits research proposals and opening reports through an evidence-first state machine with argument maps, section contracts and dynamic expert reviewers. This is a research-writing state machine, not a generic prompt to write a paper, inspired by projects for state-and-score workflows, dynamic experts, entry questions and AI-writing cleanup. Nine principles govern it: evidence before text, with a research canon and evidence table built first; an argument map before sections; a contract for every section listing purpose, allowed claims, forbidden claims, inputs and validation; scope before completeness; experts summoned by failure mode; content before language polish; no upgrading hedged claims into proof; deletion over explanation; and stopping on a plateau, expert conflict or missing evidence.

When should I use Researchwrite Proposal Pipeline?

Researchwrite Proposal Pipeline fits situations like: drafting a Chinese research proposal or opening report from a topic; revising an existing proposal against an evidence table; auditing a proposal for unsupported claims and scope creep; exporting a finished proposal project to Markdown or Word.

How do I install Researchwrite Proposal Pipeline in Claude Code?

Run `npx skills add Yuan1z0825/nature-skills --skill researchwrite -a claude-code`. Or copy the skill folder (skills/nature-proposal-writer in Yuan1z0825/nature-skills) into .claude/skills/researchwrite in your project. Claude Code loads it when a task matches its description.

How do I install Researchwrite Proposal Pipeline in Codex?

Run `npx skills add Yuan1z0825/nature-skills --skill researchwrite -a codex`. Or copy the skill folder (skills/nature-proposal-writer in Yuan1z0825/nature-skills) into .agents/skills/researchwrite in your project. Codex loads it when a task matches its description.

Can I use Researchwrite Proposal Pipeline in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yuan1z0825/nature-skills --skill researchwrite -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/researchwrite, .gemini/skills/researchwrite, .github/skills/researchwrite and .opencode/skills/researchwrite in your project.

What does Researchwrite Proposal Pipeline need to run?

SKILL.md names no scripts, command-line tools or credentials: Researchwrite Proposal Pipeline is instructions for the agent only.

Does Researchwrite Proposal Pipeline access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Researchwrite Proposal Pipeline safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Researchwrite Proposal Pipeline use?

Researchwrite Proposal Pipeline is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Researchwrite Proposal Pipeline use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 19k tokens, read only when the agent opens those files.

What are the alternatives to Researchwrite Proposal Pipeline?

Skills that share tags, products or a category with Researchwrite Proposal Pipeline: Anti-Defensive Writing Editor (Kiterlin/anti-defensive-writing, 861 stars), NSFC Research Plan Writer (HuiyuLi-2000/Chinese-Grant-Writer-Skills, 434 stars), Academic Humanizer (AIScientists-Dev/academic-humanizer, 1.9k stars) and NSFC Abstract Writer (huangwb8/ChineseResearchLaTeX, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Researchwrite Proposal Pipeline?

Yuan1z0825 (a GitHub user) maintains it in Yuan1z0825/nature-skills, which has 46,752 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: Yuan1z0825/nature-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.