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Paper Bootstrap

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Boom5426
paper-bootstrap

Use when starting a new manuscript project or cleaning up an existing paper directory and you need a standard structure, active source files, project memory, and venue defaults before deeper writing begins.

Overview

PublisherBoom5426
RepositoryNature-Paper-Skills
Skill namepaper-bootstrap
Stars
570
Forks
48
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by Boom5426 on GitHub. Read the source before you install it.

Installation

Install the Paper Bootstrap 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/Boom5426/Nature-Paper-Skills/tree/main/skills/core/paper-bootstrap
  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/Boom5426/Nature-Paper-Skills.git /tmp/Nature-Paper-Skills
mkdir -p .claude/skills
cp -r /tmp/Nature-Paper-Skills/skills/core/paper-bootstrap .claude/skills/paper-bootstrap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Bootstrap 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 Paper Bootstrap 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 Paper Bootstrap 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.

Paper Bootstrap

Overview

Use this skill to make a paper project writable before trying to make it good. The goal is not to generate boilerplate. The goal is to identify the real manuscript source of truth, create only the folders that help the workflow, and lock the project onto the correct venue and contribution framing early.

Default assumption: broad-impact journal work should start from the global Nature-style preference unless the user explicitly overrides the venue.

When the manuscript depends on experiments, figures, or results produced across multiple rounds, initialize lightweight project-state documents early so writing does not depend on memory alone: project_truth.md, decision_log.md, result_summary.md, and paper_handoff.md.

Use the helper script when you need a clean baseline layout quickly:

bash
python ~/.agents/skills/paper-bootstrap/scripts/init_paper_layout.py path/to/paper
# Claude Code (global install): replace ~/.agents/skills with ~/.claude/skills
# Claude Code (project-local install): replace ~/.agents/skills with .claude/skills

When To Use

Use this skill when:

  • A new paper directory is being created
  • An existing paper directory has drafts, figures, notes, and outputs scattered across multiple places
  • It is unclear which file is the active manuscript source of truth
  • The project has no paper-specific memory yet
  • The writing workflow needs a clean starting structure before revision begins

Do not use this skill for:

  • Late-stage section polishing
  • Figure-by-figure logic repair inside an already stable manuscript
  • Conference-template conversion after the paper structure is already settled

Bootstrap Order

  1. Inspect the current paper directory before creating anything.
  2. Identify the active manuscript source of truth.
  3. Classify the paper's contribution type:
    • method
    • framework
    • benchmark
    • resource
    • biological finding
    • mixed, with one clearly dominant contribution
  4. Lock the working venue style:
    • default to Nature-style unless the user says otherwise
    • record venue-specific overrides only if they materially affect structure
  5. Create only the directories that the project actually needs.
  6. Create or reuse lightweight state files when they will reduce drift:
    • notes/project_truth.md
    • notes/decision_log.md
    • notes/result_summary.md
    • notes/paper_handoff.md
  7. Save or update project memory with the stable local constraints.
  8. End with a short startup summary:
    • active manuscript file
    • dominant contribution type
    • venue style
    • missing assets or unresolved ambiguity
    • recommended next revision step

Preferred Project Layout

Do not force this layout mechanically. Use it as the default skeleton when the repository is messy or empty.

  • input/ for external source material, reviewer comments, and imported files
  • notes/ for working notes, claim maps, figure plans, meeting summaries, and lightweight state files
  • figures/ for source figures and editable figure assets
  • output/doc/ for active manuscript drafts and exported versions
  • output/review/ for audits, revision memos, and pre-submission checks

Default state files inside notes/ when the project does not already have equivalents:

  • project_truth.md for the current paper story, active manuscript, central claim, and stable constraints
  • decision_log.md for durable framing or scope decisions that future revisions must respect
  • result_summary.md for experiment-to-manuscript translation of supported findings, weak findings, and figure anchors
  • paper_handoff.md for the current writing-ready package: what is ready to draft, what still lacks evidence, and which figures or tables carry each point

If the project already has an established structure, preserve it and map the workflow onto that structure instead of rebuilding everything.

Memory Rules

Write project memory only for stable local facts such as:

  • the manuscript's dominant contribution type
  • persistent figure-role constraints
  • project-specific terminology
  • local preferences that do not generalize beyond the paper

Do not write project memory for:

  • transient to-do lists
  • single-round wording preferences
  • one-off tactical decisions that are unlikely to matter next week

Common Mistakes

  • Creating a directory tree before checking what already exists
  • Treating every paper like a methods paper when the real contribution is a framework or benchmark
  • Leaving multiple draft files active with no declared source of truth
  • Carrying conference-style structure into a broad-impact journal manuscript by default
  • Writing directly from remembered experiment outcomes instead of refreshing result_summary.md and paper_handoff.md
  • Saving local clutter into memory instead of only the stable constraints
  • Carrying process status from notes/ ("locked", "frozen", "verified", gate verdicts) into manuscript prose; the notes record how the work was run, the manuscript states what it found

Output Standard

When you finish bootstrapping, report:

  • active source file
  • chosen venue style
  • contribution type
  • directories created or reused
  • state files created or reused
  • project-memory items saved
  • next concrete writing step

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 Paper Bootstrap AI skill do?

Use when starting a new manuscript project or cleaning up an existing paper directory and you need a standard structure, active source files, project memory, and venue defaults before deeper writing begins.

Why use Paper Bootstrap on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Boom5426/Nature-Paper-Skills/tree/main/skills/core/paper-bootstrap. 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 Paper Bootstrap?

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 Paper Bootstrap?

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

Is the Paper Bootstrap AI skill free?

Yes. It is published on GitHub by Boom5426 under the MIT license. 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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