nature-reader
Yuan1z0825/nature-skills · Agent Skill
Creates bilingual, structured Markdown readers for academic papers from PDF, DOI, or HTML, preserving figures, tables, and equations.
Jump to install ↓Overview
| Publisher | |
|---|---|
| Repository | Yuan1z0825/nature-skills ↗ |
| Skill folder | nature-reader |
| Stars | 39,583 |
| Bundled files | 2 |
| License | Apache-2.0 |
| Last updated | August 7, 2026 |
| Links | Skill source ↗ · Repository ↗ |
Mozgi check
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Installation
A skill is a folder with a SKILL.md file. Pick your agent and run the commands in a terminal (macOS, Linux or Git Bash on Windows) from your project root.
Copy the prompt into Claude, Codex or another assistant: it will review the skill and install it for you.
Install the “nature-reader” skill from mozgi.io (https://mozgi.io/skills/nature-reader/) for me. 1. Download the archive: https://mozgi.io/skills/nature-reader/download If it fails, take the skill folder from the source: https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-reader 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “nature-reader” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/nature-reader/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .claude/skills cp -r /tmp/nature-skills/skills/nature-reader .claude/skills/nature-reader
Restart Claude Code — the skill appears in the list; call it manually with /nature-reader. For all projects use ~/.claude/skills.
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .agents/skills cp -r /tmp/nature-skills/skills/nature-reader .agents/skills/nature-reader
Codex looks for skills in .agents/skills from the current folder up to the repo root; call one with /skills or $nature-reader. For all projects use ~/.agents/skills.
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .cursor/skills cp -r /tmp/nature-skills/skills/nature-reader .cursor/skills/nature-reader
Cursor picks the skill up on start; call it with “/” in Agent chat. Cursor also reads .claude/skills and .agents/skills. For all projects use ~/.cursor/skills.
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .gemini/skills cp -r /tmp/nature-skills/skills/nature-reader .gemini/skills/nature-reader
Gemini CLI offers to activate the skill when a task matches — you approve it. For all projects use ~/.gemini/skills.
Read SKILL.md and any companion files before deciding whether to install. Click a file to preview it.
SKILL.md contents
Original author text · read-onlyFull-Paper Markdown Reader — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (core principles, the reading workflow, the output contract, and per-source-format extraction guidance). - A dynamic layer (this file plus
manifest.yaml) that detects the request's source format and loads only the fragments needed for the current job.
Do not try to apply the reading logic from memory or from this router. Always load fragments from disk as described below.
Routing protocol
Follow these five steps every time the skill is invoked.
1. Load the manifest and the core layer
Read manifest.yaml ↗. It declares the source_format axis, the allowed values, and the file paths each value maps to.
Also read every file listed under always_load. These hold the core principles, the reading workflow, and the output contract that apply to every reading job, plus the shared Terminology Ledger used to build the recurring-term table.
2. Detect the source format
Decide the source_format value using the manifest's detect: hint and the user's input:
pdf-text— selectable-text PDF. Default.scanned-pdf— image-only or OCR-required PDF.html— publisher or preprint HTML page.doi-arxiv— a bare DOI or arXiv link that must be resolved first.pasted-text— pasted prose or notes with no retrievable original layout.
State the detected value in one short line to the user before processing, so they can correct you cheaply. A source may map to more than one value (for example a DOI that resolves to a PDF); load the resolution fragment first, then the fragment for the resolved artifact.
3. Load the matching fragment(s)
Read the file mapped for the detected source_format. Do not read every fragment in static/. Load only what step 2 selected.
4. Build the reader using the loaded material
Apply the loaded fragments in this priority order:
1. Core principles (core/principles.md) — bilingual reader by default, translate for meaning, never degrade to a summary, copyright caution.
2. Source-format fragment — how to extract text, figures, and tables for this input.
3. Reading workflow (core/workflow.md) — the six-step source-map-first process.
4. Output contract (core/output-contract.md) — required files and the pre-response verification checklist.
Build the Terminology Ledger as you translate (../nature-shared/core/terminology-ledger.md); it becomes the paper.md recurring-term table and the source_map.json glossary.
If constraints prevent full processing, still create a draft reader and label missing pages, figures, or low-confidence crops in translation_notes.md. Do not switch to summary mode.
5. Reach for references only when needed
The files under references/ are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest:
- detailed figure/table cropping and placement →
references/figure-extraction.md. - exact field schema for
paper.md/source_map.json→references/output-spec.md. - equations, mathematical expressions, chemical formulae, or image-only formulae →
references/equation-handling.md. - answering follow-up questions with source citations →
references/grounding-rules.md.
Why this split
- The static layer is versioned and reviewable. Adding a new source format is one new fragment plus one manifest line.
- The dynamic layer keeps each invocation cheap: only the fragment relevant to this input enters context.
- The router itself is short on purpose. Update fragments, not this file, when adding scope.
- This structure mirrors
nature-writingandnature-polishingso shared content lives innature-shared/.
Frequently asked questions
What does the nature-reader skill do?
Creates bilingual, structured Markdown readers for academic papers from PDF, DOI, or HTML, preserving figures, tables, and equations.
How do I install nature-reader?
Open the “Installation” block on this page and pick your agent — Claude Code, Codex, Cursor or Gemini CLI — for ready-made commands that copy the skill folder to the right place. Or copy the prompt from the “Via assistant” tab and let the assistant review and install the skill.
Which AI models work with nature-reader?
Skills are plain Markdown instructions (the Agent Skills format), so nature-reader works with any agent that reads SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and more. The model loads the instructions only when a task matches the skill's description.
Is nature-reader free?
The skill is published openly by Yuan1z0825 under the Apache-2.0 license. The skill itself costs nothing — you only pay your AI provider for the tokens you use.
Is it safe to install skills?
A skill is a set of instructions your agent will follow, so read SKILL.md and its companion files before installing. Scripts in the skill folder only run if your agent runs them — keep an eye on what it does and do not grant unnecessary permissions.
How is a skill different from an MCP server or a plugin?
An MCP server or plugin gives the model new tools — code that runs and returns a result. A skill gives it knowledge and process: how to approach a task, which steps to follow and what good output looks like. It needs no server or API key.