Research Paper logo

Research Paper

Community
luwill
research-paper

Use when the user asks to write or draft an ORIGINAL RESEARCH ARTICLE — IMRaD paper, conference paper, short/workshop paper, 研究论文/期刊论文/会议论文 — reporting their own completed experiments, in English or Chinese. Triggers: "write the paper", "draft our manuscript", "把我们这篇工作写成论文", "论文写作". Results-first: the user's actual results (tables, metrics, figures) are the raw material; this skill never invents an experiment or a number. NOT for literature reviews/surveys (use medical-imaging-review), NOT for proposals (use research-proposal), NOT for grant applications.

Overview

Publisherluwill
Repositoryresearch-skills
Skill nameresearch-paper
Stars
863
Forks
100
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Research Paper 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/luwill/research-skills/tree/main/research-paper
  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/luwill/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/research-paper .claude/skills/research-paper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Paper 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 Research Paper 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 Research Paper 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.

Research Paper Writer

Draft an original research article from the user's actual completed work: their experiments, data, figures, and references. Two disciplines govern everything:

  1. Results-first. Methods and Results are written from materials the user supplied (result tables, metrics dumps, figures, code notebooks). Anything not supplied is flagged [PENDING: …] — never filled with a plausible number. A fabricated result is worse than a fabricated citation: it is data fraud, not a style slip.
  2. Citation integrity (this repository's shared five-rule core, instantiated for author-year/numeric journal styles — see references/CITATION_INTEGRITY.md): every reference verified to exist before it enters the bibliography; [UNVERIFIED] flags for the rest; no invented DOIs, authors, or numbers.

Scenario routing

Study typeStructureBinding checklist (Phase 1)
Imaging-AI method / model paperIMRaD, methods-forwardCLAIM 2024
Prediction / risk modelIMRaD + model development emphasisTRIPOD+AI
Diagnostic accuracy studyIMRaD, test-vs-reference emphasisSTARD 2015 (可与 CLAIM 并行声明)
Observational / cohort studyIMRaD, methods after hypothesesSTROBE
(Rare here) trial, systematic reviewOut of scope — say so and stopCONSORT / PRISMA via the right skill
CS/eng venue paper (CVPR/NeurIPS style)Abstract → Intro → Related Work → Method → Experiments → Discussion → Conclusionvenue's own checklist/reproducibility statement

Read references/STRUCTURE_GUIDE.md for section-by-section construction and references/REPORTING_CHECKLISTS.md for what the binding checklist actually requires.

Phase 1: Intake (record, don't improvise)

Use AskUserQuestion to collect:

  1. Study type + target venue/journal — routes structure and checklist (above).
  2. Materials in hand — result tables/CSVs, metric numbers, figures, prior drafts/notes, code notebook. If the user has no results yet, stop: this skill writes papers about completed work, not imagined work (that is research-proposal territory).
  3. References pool — user's Zotero/bib, or run the lit-search skill for a defensible related-work corpus (its references.bib drops straight in).
  4. Language and length — en/zh; word limit from the venue's guide.

Record the intake as a short paper_plan.md (study type, venue, checklist, materials list, citation style). Headless runs: record inline and continue.

Phase 2: Materials audit (before any prose)

Inventory every number that will appear in Results:

  • Each metric → its source artifact (file, table, cell). Untraceable numbers are [PENDING], and the user is told before drafting starts.
  • Each figure → real file or a [PENDING: figure] placeholder with a content description. Redrawing/schematics follow the same honesty rules as scholar-slides (label what is not data).
  • This inventory becomes results_ledger.csv (see references/QUALITY_CHECKLIST.md) — the paper-side sibling of the review skill's claim ledger: one row per load-bearing number, claim_id → section → claim_text → value(列名以 references/QUALITY_CHECKLIST.md 的 schema 表头为准).

Phase 3: Outline + approval (red line)

Section skeleton per references/STRUCTURE_GUIDE.md, with per-section bullet content mapped to Phase 2 materials. Present the outline and wait for explicit approval (same rules as research-proposal: "你看着办" → state assumptions, re-present, proceed).

Phase 4: Write-with-verify (per section)

Draft section by section into a single working file paper_draft.md (the Phase 5 gates grep exactly that filename — a renamed draft makes them scan an empty file and report a false zero-hit); after each section, before starting the next:

  1. Every citation in the section is from the verified pool (rules: references/CITATION_INTEGRITY.md).
  2. Every number in the section reconciles against results_ledger.csv.
  3. Self-check against the AI-tell list and style rules (references/WRITING_STYLE.md) — the Phase 5 grep gates make these checkable, not aspirational.
  4. Tense/register per section (Methods: past, what was done; Results: past, what was found — no interpretation; Discussion: present, what it means).

Phase 5: Gates + reporting checklist + submission pack

Mechanical gates (paste command output as evidence):

bash
# 1) Placeholder gate — zero hits (or disclosed [UNVERIFIED]/[PENDING])
grep -nE "xxx|XXXX|\[TBD\]|\[PENDING|\[UNVERIFIED[^]]*\]|10\.xxxx|doi:10\.[a-z]+/x" paper_draft.md

# 2) AI-tell gate — zero hits
grep -nEi "over the past ((few|several|two|three) )?(decades?|years)|in recent years,|it is worth noting|plays a (crucial|pivotal) role|has garnered significant attention|delves into|a testament to|众所周知|近年来,?随着|日益受到(广泛)?关注" paper_draft.md

Then: work through the binding reporting checklist item by item (references/REPORTING_CHECKLISTS.md); assemble the submission pack — title page, abstract in the venue's format, keywords, cover letter, author contributions (CRediT) / conflicts (references/SUBMISSION_PACK.md).

Reference Files

Read only the file for the phase you are in.

FileRead when
references/STRUCTURE_GUIDE.mdPhase 3 — IMRaD construction per study type
references/CITATION_INTEGRITY.mdPhase 2/4/5 — the five rules + Phase 5 reconciliation gate
references/REPORTING_CHECKLISTS.mdPhase 1/5 — checklist selection + item-by-item pass
references/WRITING_STYLE.mdPhase 4 — register, tense, cohesion, AI-tells
references/SUBMISSION_PACK.mdPhase 5 — abstract/cover letter/reproducibility statement
references/QUALITY_CHECKLIST.mdPhase 2/5 — Phase 2 reads the ledger schema; Phase 5 runs the gates
assets/paper_scaffold_en.mdPhase 4 — English skeleton
assets/paper_scaffold_zh.mdPhase 4 — 中文骨架

learnings:复盘先于重写

修改 references 或 scaffold 之前先扫 learnings/;返工或发现 自相矛盾后追加一篇,日期命名(格式见 lit-search/learnings/)。

Error handling

  • No results materials — stop and say so; suggest research-proposal if the work is still planned.
  • A claimed number can't be traced — [PENDING] + explicit disclosure, never a placeholder that looks real.
  • Reference can't be verified — [UNVERIFIED] + disclosure (never dropped silently, never fabricated).
  • Venue requires a checklist this skill doesn't carry — say so, point to the official source, and record it as a manual gate in paper_plan.md.

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 Research Paper AI skill do?

Use when the user asks to write or draft an ORIGINAL RESEARCH ARTICLE — IMRaD paper, conference paper, short/workshop paper, 研究论文/期刊论文/会议论文 — reporting their own completed experiments, in English or Chinese. Triggers: "write the paper", "draft our manuscript", "把我们这篇工作写成论文", "论文写作". Results-first: the user's actual results (tables, metrics, figures) are the raw material; this skill never invents an experiment or a number. NOT for literature reviews/surveys (use medical-imaging-review), NOT for proposals (use research-proposal), NOT for grant applications.

Why use Research Paper on TypingMind?

Because you install it once and use it with any model. Research Paper 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 Research Paper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luwill/research-skills/tree/main/research-paper. 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 Research Paper?

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 Research Paper?

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

Is the Research Paper AI skill free?

It is published on GitHub by luwill. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇