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 pass
- 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 | $0.00061 | $0.00801 |
| Opus 5.5 | $0.00024 | $0.00320 |
| Sonnet 5.5 | $0.00012 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
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 1mo 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, 35 lines differ
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Search — Router
Routing protocol
For local citation-file conversion with complete supplied records, load
references/workflows/wf4-citation-file-mgmt.md and its format reference directly.
Source lookup, API setup, and network preflight apply only when retrieval or verification is
needed; do not require them for a requested offline conversion.
For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.
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)
Read the file mapped for each detected workflow (under references/workflows/). Do not read every workflow. Each workflow file links to the shared modules it needs.
What ships with it
53 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 134 B
- mcp-server/sources/__init__.py 352 B runs code
- mcp-server/sources/arxiv.py 11 KB runs code
- mcp-server/sources/crossref.py 6.9 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_arxiv_error_entries.py 1.4 KB runs code
- mcp-server/tests/test_citation_errors.py 1.6 KB runs code
- mcp-server/tests/test_elsevier_live.py 2.6 KB runs code
- mcp-server/tests/test_format_converter_hardening.py 4.2 KB runs code
- mcp-server/tests/test_mcp_tools.py 2.6 KB runs code
- mcp-server/tests/test_pubmed_parsing.py 894 B runs code
- mcp-server/tests/test_scopus_empty_result.py 1006 B runs code
- mcp-server/tests/test_script_converters.py 2.9 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 1.9 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 19 KB runs code
- scripts/format-converter.py 20 KB runs code
- scripts/preflight.py 3.7 KB runs code
- static/core/routing-and-ops.md 5.0 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.
- 1mo ago Changed · -19 lines · -150 tokens per session 3ed327e3e9c5
- 1mo 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 (50,803 stars, last pushed 2d ago), licensed Apache-2.0. It adds 61 tokens to every session and 801 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.
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.
matlab-orchestrator
MATLAB agentic workflow router. Use this whenever the user asks for MATLAB, Simulink, .m/.mlx/.slx work, toolbox-driven engineering, paper reproduction, simulation, code generation, data analysis, control, robotics, signal/image/AI workflows, or wants an end-to-end automated MATLAB task with validation. For Simulink…
matlab-runner
Run MATLAB tasks from Codex with reliable MCP-based execution, logs, artifacts, toolbox inventory, smoke tests, and reproducible validation. Use whenever MATLAB code, .m/.mlx scripts, MATLAB tests, local toolbox probing, or command-line automation is needed.
matlab-simulink-modeling
MATLAB and Simulink workflow for creating, inspecting, modifying, simulating, validating, and exporting .slx models. Use for Simulink, Stateflow, Simscape, model-based design, parameter sweeps, signal logging, requirements, and simulation automation. On this machine, Simulink tasks should be visual-first: open the…
manim-animator
Create code-driven explanatory animations and precise vector graphics with Manim, optionally using SandBase for generated images, narration, music, or transcription. Use for mathematical, scientific, algorithmic, or diagrammatic animation; use a video-generation workflow for photoreal footage.
matlab-robotics-autonomy
MATLAB robotics and autonomy workflow for ROS Toolbox, Robotics System Toolbox, Navigation Toolbox, UAV Toolbox, sensor fusion, trajectory planning, simulation, and reproducible robot experiments. Use for ROS/ROS 2 bags, robot models, path planning, SLAM, state estimation, and autonomy prototyping.