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.
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.
npx skills add Yuan1z0825/nature-skills --skill nature-academic-searchgit clone --depth 1 https://github.com/Yuan1z0825/nature-skillsWrote 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.
[](https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-academic-search)<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-academic-search"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-academic-search/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.
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-academic-search"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-academic-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00211 | $0.01119 |
| Opus 5 | $0.00105 | $0.00560 |
| Sonnet 5 | $0.00042 | $0.00224 |
| Haiku 4.5 | $0.00021 | $0.00112 |
Grade A, and why
nature-academic-search 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 13d ago.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- nature-academic-search — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Search — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (the MCP tool inventory and shared modules, and source routing plus operational rules). - A dynamic layer (this file plus
manifest.yaml) that detects which workflow the user needs and loads that workflow, reaching for shared modules and scripts only when a step needs them.
Do not try to apply the search 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 workflow axis, the allowed values, and the file paths each value maps to.
Also read every file listed under always_load:
static/core/tools.md— the MCP tool inventory (core search, extended search, PubMed utilities) and the shared-module map.static/core/routing-and-ops.md— the T1→T2→T3 source routing quick guide, environment setup, error handling, and limitations.
2. Detect the workflow
Map the user's need to one or more workflow values:
multi-source-search— find literature across sources.citation-verification— verify citations extracted from a document.mesh-strategy— build a MeSH/PubMed search strategy.citation-file-mgmt— convert/manage.nbib/.ris/.bibfiles.reference-mgmt— BibTeX, related-article discovery, ID conversion.strict-other-citation-impact-audit— determine strict independent other-citations, build article-level citation metric tables, identify high-profile citers (academy members, presidents/deans, talent-award holders, fellows, field leaders), and extract how they cited the target paper.
A combined request (for example search then export) may need more than one. State the detected workflow(s) in one short line before proceeding.
3. Load the matching workflow fragment(s)
What ships with it
47 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 265 B
- config/mcp-snippet.json 502 B
- config/settings-snippet.json 59 B
- config/triggers-academic-search.toml 978 B
- install.sh 4.3 KB runs code
- manifest.yaml 3.1 KB
- mcp-server/academic_search_server.py 21 KB runs code
- mcp-server/config.toml 249 B
- mcp-server/README.md 1.9 KB
- mcp-server/requirements.txt 109 B
- mcp-server/sources/__init__.py 352 B runs code
- mcp-server/sources/arxiv.py 11 KB runs code
- mcp-server/sources/crossref.py 6.8 KB runs code
- mcp-server/sources/elsevier_common.py 2.4 KB runs code
- mcp-server/sources/pubmed.py 13 KB runs code
- mcp-server/sources/sciencedirect.py 6.9 KB runs code
- mcp-server/sources/scopus.py 19 KB runs code
- mcp-server/tests/__init__.py 40 B runs code
- mcp-server/tests/test_elsevier_live.py 2.6 KB runs code
- mcp-server/tests/test_mcp_tools.py 2.6 KB runs code
- mcp-server/tests/test_sources.py 30 KB runs code
- mcp-server/utils/__init__.py 359 B runs code
- mcp-server/utils/config.py 1.4 KB runs code
- mcp-server/utils/errors.py 620 B runs code
- mcp-server/utils/logging.py 1.1 KB runs code
- README_EN.md 2.7 KB
- README.md 2.4 KB
- references/citation-parser.md 2.7 KB
- references/dedup-engine.md 1.9 KB
- references/pubmed-28344011.bib 3.3 KB
- references/pubmed-28344011.nbib 12 KB
- references/pubmed-28344011.ris 3.4 KB
- references/ris-bibtex-format.md 4.5 KB
- references/search-strategy.md 2.0 KB
- references/source-tiers.md 2.7 KB
- references/workflows/wf1-multi-source-search.md 1.9 KB
- references/workflows/wf2-citation-verification.md 1.6 KB
- references/workflows/wf3-mesh-strategy.md 538 B
- references/workflows/wf4-citation-file-mgmt.md 1.4 KB
- references/workflows/wf5-reference-mgmt.md 1.3 KB
- references/workflows/wf6-strict-other-citation-impact-audit.md 11 KB
- scripts/academic_search.py 20 KB runs code
- scripts/converters.py 18 KB runs code
- scripts/format-converter.py 19 KB runs code
- scripts/preflight.py 3.7 KB runs code
- static/core/routing-and-ops.md 4.9 KB
- static/core/tools.md 3.0 KB
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.
- 13d ago First seen · 75 lines · 211 tokens per session scan A 65f44bb5d31f
nature-academic-search is a skill published in the GitHub repository Yuan1z0825/nature-skills (40,913 stars, last pushed 2d ago), licensed Apache-2.0. It adds 211 tokens to every session and 1,119 once invoked, about $0.0011 per session on Opus 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.
Other skills, from other repositories
literature-searcher
Search CrossRef, OpenAlex, PubMed, Semantic Scholar, and optional Scopus; deduplicate results, download open-access PDFs by DOI, classify papers, monitor new results, and analyze coverage. Use when asked to search literature, monitor a topic, download an open-access paper, classify papers, or analyze literature gaps.
academic-research
Search academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence gathering.
academic-trend-research
Research emerging academic trends by combining scholarly databases, web sources, and news to identify rising research areas, breakthrough papers, and shifting scientific consensus. Ideal for R&D teams tracking frontier science.
using-cesiumjs-skills
Use when starting any conversation involving CesiumJS development - provides orientation on available domain skills and how they activate.
latex-empirical-tables
Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide…
cite-placement
Place pre-screened literature citations into a LaTeX or Word manuscript, or restyle the citations already in one. Three modes: (1) inline placement — inline \cite{}/\citet{}/\citep{} with a compiled references.bib, for author-date journals (APA, MLA, Harvard, Chicago author-date, IEEE, Vancouver); (2) footnote…