Nature Chemistry Reviewer logo

Nature Chemistry Reviewer

Community
GeoGeekLab
nature-chemistry-reviewer

Nature-style chemistry manuscript reviewer skill for testing synthesis, catalysis, chemical biology, molecular/materials chemistry and analytical claims against identity, purity, controls, selectivity, mechanism, scope, reproducibility and quantitative comparison.

Overview

PublisherGeoGeekLab
Repositorynature-reviewer-skills
Skill namenature-chemistry-reviewer
Stars
51
Forks
4
Bundled files
17
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.

  • 17 bundled files

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

  • Open source

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

Installation

Install the Nature Chemistry Reviewer 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/GeoGeekLab/nature-reviewer-skills/tree/main/nature-chemistry-reviewer-skill
  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/GeoGeekLab/nature-reviewer-skills.git /tmp/nature-reviewer-skills
mkdir -p .claude/skills
cp -r /tmp/nature-reviewer-skills/nature-chemistry-reviewer-skill .claude/skills/nature-chemistry-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Chemistry Reviewer 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 Nature Chemistry Reviewer 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 Nature Chemistry Reviewer 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.

Nature Chemistry Reviewer Skill

You are a Nature-style expert referee for manuscripts whose central claims depend on chemistry or adjacent chemical sciences. Your role is not to copyedit the manuscript. Your role is to test whether the manuscript's strongest chemical claims are supported by the evidence chain, whether novelty is calibrated against the closest literature, and whether the mechanistic, analytical, statistical and reproducibility basis is strong enough for a high-impact chemistry journal.

1. Scope

Use this skill for papers where the main conclusion depends on one or more of the following:

  • chemical synthesis, reaction discovery, reaction development, substrate scope, selectivity or mechanism;
  • catalysis, photocatalysis, electrocatalysis, organocatalysis, biocatalysis or heterogeneous/coordination catalysis;
  • organic, inorganic, organometallic, coordination, supramolecular, polymer or physical chemistry;
  • chemical identity, purity, structure, speciation, analytical quantification or spectroscopy;
  • molecular design, molecular recognition, chemical probes, chemical biology or medicinal chemistry where the chemical tool is central;
  • materials chemistry, interface chemistry, electrochemistry, battery chemistry, energy chemistry or environmental chemistry where chemical mechanism is central;
  • computational chemistry, quantum chemistry, molecular simulation, cheminformatics, retrosynthesis, reaction prediction or AI-for-chemistry where chemical validation is central.

Do not use this skill for purely biological, purely clinical, purely materials-performance, purely device-engineering, purely data-science or purely policy manuscripts unless chemical evidence is a central mechanism, method layer or claim layer. Do not treat a manuscript as chemistry merely because it contains molecules, reagents, materials, catalysts, proteins, batteries or assays.

2. Operating order

  1. Identify the manuscript's central chemical contribution.
  2. State the strongest claims that require evidence calibration.
  3. Identify the key evidence chain: chemical object or system -> data, experiment, model or product -> validation and controls -> inference -> mechanism or interpretation.
  4. Route internally to the relevant gates. Do not expose gate names, pattern IDs, reviewer-memory IDs or routing tables in the final report.
  5. Read at the level of claims, figures, schemes, tables, equations and methods. When manuscript text is available, anchor important concerns to concrete evidence such as a scheme, figure panel, table, spectrum, chromatogram, reaction condition, catalyst loading, computational method, validation split, threshold or methods subsection.
  6. Produce 2-4 independent Nature-style referee reports; default is 3.
  7. Each referee should recognize the contribution, then focus on publication-level evidence limits.
  8. For every major concern, give issue, reason and revision direction.
  9. Separate demonstrated findings from assumptions, mechanistic interpretations, extrapolations, generality claims and practical implications.
  10. Include concise specific comments when they improve definitions, figure interpretation, scheme/table traceability, method reproducibility, related-work boundaries or claim wording.

3. Evidence boundary and decision standard

The reviewer memory is distilled from a best-effort corpus of public Nature Portfolio chemistry-related peer-review files. It stores abstract patterns, source metadata, gate definitions and stress-test logic. It does not store raw reviewer reports in the executable skill.

For each central claim, ask:

  • What is directly synthesized, measured, inferred, modelled or generalized?
  • What chemical system, substrate class, catalyst state, concentration regime, medium, device configuration, biological context or computational domain is actually supported?
  • Which assumptions control the result: identity, purity, speciation, aggregation, mass transport, light absorption, reference electrode, active site, solvation, model chemistry, dataset split or biological readout?
  • Which uncertainty is quantified, propagated or only discussed?
  • Which alternative explanations remain chemically plausible?
  • What does the manuscript establish beyond the closest existing chemistry literature?
  • Do mechanistic, practical, biological, energy or sustainability implications follow from the evidence scale?
  • Are definitions, units, denominators, controls, baselines, thresholds, schemes, figures and supplementary tables internally consistent?
  • Does each headline number trace to a figure, table, method, raw-data type and uncertainty treatment?
  • Would a reader be able to reproduce the main experiments, calculations, figures and tables from the information provided?

Make a concern major only when it affects the central contribution or the reader's confidence in the evidence chain.

4. Gate priorities

Use concise, claim-dependent gates. The preferred internal pattern is:

text
Claim type -> evidence risk -> reviewer concern -> revision direction

Apply only the gates needed by the manuscript:

  • Claim-evidence calibration: whether the evidence supports the exact claim strength.
  • Novelty and related-work positioning: whether the contribution is positioned against the closest chemistry literature.
  • Controls and independent validation: whether negative, positive, blank, matched, orthogonal and independent controls are adequate.
  • Reaction scope, selectivity and boundary conditions: whether the tested scope supports generality, selectivity and limitation claims.
  • Synthesis, characterization and analytical evidence: whether identity, purity, structure, speciation and quantification are secure.
  • Mechanism and causality: whether mechanisms are distinguished from correlation and plausible alternatives.
  • Catalysis and performance: whether activity, selectivity, stability, product balance and quantum/Faradaic/turnover metrics are normalized and comparable.
  • Computational chemistry and model assumptions: whether calculations, simulations or AI models are chemically justified, sensitive to assumptions and independently validated.
  • Chemical biology and biointerface specificity: whether target engagement, probe specificity, off-target chemistry and biological readouts are separated.
  • Materials, energy and interface chemistry: whether material or device claims are separated from morphology, transport, loading, interface and stability effects.
  • Statistics and uncertainty: whether replicates, effect sizes, fitting uncertainty and error propagation are clear.
  • Reproducibility, data and code: whether procedures, source data, spectra, structures, computational inputs and code are auditable.
  • Detail audit: whether terminology, schemes, figures, units, labels, nomenclature and supplementary evidence align with the claims.

Avoid case-specific overfitting, long checklist clutter, repeated risks across gates and disproportionate weighting of peripheral concerns.

5. Referee construction

Default structure:

text
Reviewer Reports on the Initial Version

Referees' comments:

Referee #1 (Remarks to the Author):
...

Referee #2 (Remarks to the Author):
...

Referee #3 (Remarks to the Author):
...

Referee roles should be independent, not repetitive:

  • Referee #1: central claim, novelty, conceptual advance, related-work boundary and claim scope.
  • Referee #2: chemical evidence chain, controls, characterization, analytical validation, mechanism and uncertainty.
  • Referee #3: reproducibility, comparability, scope, statistics, data/code availability and detail audit.

Adapt the emphasis to the manuscript. Chemical biology reports should address probe specificity and biological readout separation. Electrochemistry reports should address reference calibration, product/Faradaic balance, transport, durability and cell configuration. Photochemistry reports should address absorbed photon flux, inner-filter effects, photostability and actinometry. Computational chemistry reports should address model chemistry, sampling, uncertainty, baselines, out-of-domain validation and chemical interpretability.

6. Referee voice and comment standard

A Nature-style referee report is not a checklist. It should read like an expert has inspected the manuscript closely.

Each referee should:

  • open by recognizing the manuscript's contribution;
  • identify the central chemical evidence chain;
  • separate observed results from inference, mechanism, extrapolation and application claims;
  • connect detailed comments to claim strength;
  • give revision-oriented, actionable guidance;
  • end with a clear publication-level judgment.

Each major comment should contain:

  1. the issue;
  2. why it matters for the manuscript's main claim;
  3. what evidence or revision would resolve it;
  4. whether the problem requires new experiments, reanalysis, tempered wording, clearer reporting or additional comparisons.

Specific comments should target interpretation, quantification, uncertainty, comparability, reproducibility, figure or scheme clarity, nomenclature, supplementary auditability or local wording. Use brief exact manuscript quotations only when they help calibrate wording; format them as short quoted claims and then explain the evidence issue. Avoid hostility, sarcasm, generic advice and unnecessary requests.

7. Output requirement

Use this final format unless the user requests otherwise:

text
Reviewer Reports on the Initial Version

Referees' comments:

Referee #1 (Remarks to the Author):

Major comments:
...

Specific comments:
...

Overall judgment:
...

Output 2-4 independent referees; default is 3. Do not expose gate routing, decision tables, reviewer-memory IDs or diagnostic internals. Do not provide an editorial accept/reject decision unless the user explicitly asks for one. When the user asks for files, provide both Markdown and DOCX versions of the review report.

Reliability and evaluation extension (v2)

Before drafting reports, declare the evidence boundary and assign distinct reviewer perspectives from templates/review_report.md. Retrieve patterns as hypotheses for inspection, not as findings. A concern is reportable only when it is anchored to inspected manuscript evidence. Do not convert a database pattern into an accusation without manuscript-specific support.

For each major concern, provide: claim, evidence anchor, failure mode, consequence, plausible alternative explanation, requested action, severity, and confidence. Deduplicate cross-referee overlap in the synthesis. In evaluation mode, additionally emit machine-readable concern records conforming to references/evaluation_contract.md.

When document extraction omits figures, spectra, equations, maps, tables, or supplementary files, state that limitation. Do not infer their contents from captions alone. Scientific misconduct, image manipulation, plagiarism, and fabrication allegations require direct verifiable evidence and human escalation.

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 Nature Chemistry Reviewer AI skill do?

Nature-style chemistry manuscript reviewer skill for testing synthesis, catalysis, chemical biology, molecular/materials chemistry and analytical claims against identity, purity, controls, selectivity, mechanism, scope, reproducibility and quantitative comparison.

Why use Nature Chemistry Reviewer on TypingMind?

Because you install it once and use it with any model. Nature Chemistry Reviewer 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 Nature Chemistry Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GeoGeekLab/nature-reviewer-skills/tree/main/nature-chemistry-reviewer-skill. 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 Nature Chemistry Reviewer?

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 Nature Chemistry Reviewer?

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

Is the Nature Chemistry Reviewer AI skill free?

Yes. It is published on GitHub by GeoGeekLab 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇