nature-paper-card

nature-paper-card is a skill for Codex from Yuan1z0825/nature-skills. It costs 58 tokens per session (1,727 once invoked), scanned A, original, Apache-2.0.

A structured deep-reading record for one scientific paper, preprint, PDF, DOI, arXiv page, publisher article, or pasted text. It connects the paper's methods, experiments, claims, figures, and evidence instead of only summarising the abstract.

In plain words
What is it for?
Use it to analyse one paper in depth, trace experiments to claims, review figures and methods, and create a source-grounded research card.
Why use it?
It makes it easier to see how a paper's evidence supports its conclusions and to mark sections that cannot be assessed from incomplete material. It avoids treating a partial source as if it were a full paper.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to analyse one paper in depth, trace experiments to claims, review figures and methods, and create a source-grounded research card.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yuan1z0825/nature-skills/nature-paper-card
About the project

Nature Skills is a collection of reusable skills that help AI agents handle academic writing and scientific visualization. Researchers and AI-assisted scholars use it to turn research tasks into repeatable workflows and usable outputs. The catalogue entries are skills from this collection.

Yuan1z0825/nature-skills · 50,803 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Yuan1z0825/nature-skills --skill nature-paper-card
Clone the repo
git clone --depth 1 https://github.com/Yuan1z0825/nature-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for nature-paper-card

README.md
[![agentmods](https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-paper-card/github.svg)](https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper-card)
Your own site
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper-card"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-paper-card/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for nature-paper-card

Your own site · 80×15
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper-card"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-paper-card.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,727 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Sonnet 5 · 6 Sept 2026 📄 Read the review Third-party audits
  • Socket pass 14 Sept 2026
  • Snyk pass 14 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.01727
Opus 5.5 $0.00023 $0.00691
Sonnet 5.5 $0.00012 $0.00345
Haiku 4.5 $0.00006 $0.00173

Measured 1mo ago against content hash a71e3dbc4465, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

nature-paper-card scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/audit_paper_card.py, scripts/prepare_paper.py, tests/test_audit_page_ranges.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/nature-paper-card/SKILL.md · 159 lines

How it starts

The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Nature Paper Card - Router

Use this skill to turn one paper into an evidence-grounded research card, not a translated abstract, generic summary, reviewer report, or publication article.

The skill uses:

  • a static core under static/core/ for principles, workflow, and the fixed output contract;
  • one paper-type fragment under static/fragments/paper_type/;
  • on-demand references for evidence labels, the exact card schema, and research-idea checks.

Routing protocol

Follow these steps every time.

1. Load the manifest and core layer

Read manifest.yaml, then read every file under always_load. Do not generate the card from this router alone.

2. Establish the source boundary

Identify which material is available:

  • full paper with figures and tables;
  • paper text without reliable layout;
  • abstract or metadata only;
  • an existing nature-reader artifact with stable source IDs.

Prefer an existing nature-reader artifact when supplied. Do not repeat full bilingual translation or figure extraction. If only partial material is available, create a visibly partial card and mark every unsupported section Not assessable from supplied material.

For a PDF or nature-reader source-map JSON, the bundled script is mandatory.

  1. Resolve SKILL_DIR as the directory containing this loaded SKILL.md.
  2. Verify SKILL_DIR/scripts/prepare_paper.py exists.
  3. Run exactly the bundled script by its resolved path:
python "SKILL_DIR/scripts/prepare_paper.py" INPUT \
  --output WORKDIR/source_bundle.json

Add --render-dir WORKDIR/rendered-pages when visual page review is needed. Inspect the script exit code and the bundle validation block before drafting.

For source-map input, also inspect locator_summary and unlocated_blocks. Only records under pages have verified positive PDF page locators. Missing or invalid page locators remain in unlocated_blocks with an explicit status and must be cited structurally, never as page 1.

Read the full file on GitHub · 159 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · -121 tokens per session a71e3dbc4465
  2. 1mo ago First seen · 159 lines · 179 tokens per session scan A 9f0bc1412059

Subscribe to this mod's changes

nature-paper-card is a skill published in the GitHub repository Yuan1z0825/nature-skills (50,803 stars, last pushed 2d ago), licensed Apache-2.0. It adds 58 tokens to every session and 1,727 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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