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Review

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Muuuun
review

Write domain-authentic review articles that synthesize rather than stack. Covers 10 scientific domains (physics, chemistry, biology, medicine, mathematics, computer_science, earth_environment, astronomy, economics, materials) with per-domain style guides mined from landmark reviews in RMP / Nat. Rev. X / Annu. Rev. / Chem. Rev. / Bull. AMS / etc. Use this skill when writing a survey / review / report that spans multiple primary papers.

Overview

PublisherMuuuun
Repositoryluxas
Skill namereview
Stars
1.2K
Forks
18
Bundled files
16
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.

  • 16 bundled files

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

  • Open source

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

Installation

Install the Review 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/Muuuun/luxas/tree/main/skills/review
  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/Muuuun/luxas.git /tmp/luxas
mkdir -p .claude/skills
cp -r /tmp/luxas/skills/review .claude/skills/review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review 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 Review 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 Review 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.

Review Skill

Stacking is the default failure mode of autonomous review projects: "Smith et al. did X, found Y. Jones et al. extended to Z..." paragraph after paragraph, with no unifying thesis and no synthesis of tensions. Top-tier reviews do the opposite — they lead with a claim, pull disparate work into one frame, and leave the reader smarter than when they started.

This skill encodes the rhetorical and structural conventions of landmark reviews across 10 domains, mined from real corpus text (RMP, Chem. Rev., Nat. Rev. X, Annu. Rev., Bull. AMS, Science / Nature Reviews, and the domain-specific tier-1 venues). Inject a domain's style guide, follow the 3-step pipeline, obey the hard rules — and the output should pass the Turing test for an editor at its target venue.

When to use which venue voice

┌──────────────────────────────────────────────────┬──────────────────┐
│  Target length / audience                        │  Voice           │
├──────────────────────────────────────────────────┼──────────────────┤
│  Monograph (50–150 pp), pedagogical, archival    │  RMP / Chem.Rev. │
│    → equations, boxes, glossary, deep derivation │  / Phys.Rep.     │
│  Specialist (20–40 pp), claim-dense, tutorial    │  Annual Reviews  │
│    → thesis-per-section, figure-rich             │                  │
│  Short assessment (5–15 pp), stance-forward      │  Nat. Rev. X /   │
│    → abstract-caliber thesis per ¶, citation-dense │  Trends / Curr.Op. │
│  Opinionated essay (5–20 pp), single author      │  Nat Comment /   │
│    → warm first-plural, polemical beat           │  Nobel lecture   │
└──────────────────────────────────────────────────┴──────────────────┘

See references/decision_tree.md for full venue taxonomy.

The 3-step pipeline (mandatory)

Skip no step. Each exists because stacker projects skip exactly this step.

Step 1 — Outline with thesis per section (BEFORE any prose)

Produce notes/report_outline.md first (canonical path — the finish-gate's outline check reads exactly this file; first line MUST be type: survey). For the annotated gold-standard skeleton, see skills/review/references/exemplar_survey_outline.md:

markdown
type: survey
# <title>

## §1 Introduction
**Thesis:** <one sentence. What is this review claiming about the field?>
**Unifying framework:** <concept / equation / taxonomy that ties everything>
**Fold in:** Paper A (framework), Paper B (canonical result), …

## §2 <topic>
**Thesis:** <claim this section argues, not a topic label>
**Fold in:** Paper C, D, E
**Synthesis move:** <comparative_table | contrast_pair | unifying_equation | …>

…

Gate: do not draft prose until a PI / reviewer / user has signed off on this outline. This is the single most effective anti-stacking move — if you cannot articulate a thesis per section in one sentence, the section will stack.

Step 2 — Draft, reading domain style guide before each section

Before drafting §N:

  1. Read style_guides/<domain>.md — the entire guide, every time, fresh.
  2. Read §N's outline entry.
  3. Write prose that obeys the guide's claim-leading, synthesis-move, and transition conventions. Quote primary papers only where a claim needs evidence, not to fill the paragraph.

Step 3 — Synthesis rewrite pass (POST-draft)

After the full draft:

  1. For every paragraph, check its first sentence. If it starts with "Smith et al." / "Jones and coworkers" / "In [42]" / any author name → rewrite it to lead with a claim about the phenomenon, and push the citation to mid-paragraph or parenthetical.
  2. For every section, check its first paragraph. If it starts with orientation ("This section discusses…") instead of thesis → rewrite the first sentence to state the claim.
  3. For every section-to-section boundary, add a transition sentence that names the bridge (cause→effect / contrast / zoom-in / caveat / historical pivot). See references/transition_moves.md.

Hard rules

These are non-negotiable. Violations should trigger rewrite before output.

  1. No paragraph begins with an authorship phrase. "Smith et al." / "Jones and coworkers" / "In [42]" as paragraph-openers are banned. Lead with a claim about the phenomenon; attribute parenthetically or mid-paragraph.
  2. No section begins with "This section…" / "We now discuss…" / "Here we…" Lead with a thesis sentence about the field.
  3. No uncited claim, no un-claiming citation. Every citation must support a specific claim. Every claim beyond common knowledge gets a citation. Drive-by citations ("for a review see [7, 8, 9]" without claim anchoring) are a stacker tell — use them sparingly and only at section boundaries.
  4. Fold, don't list. When multiple papers make compatible findings, synthesize them into one claim with a shared citation cluster. When they disagree, make the tension the subject of the paragraph.
  5. Use domain voice. If writing physics, obey style_guides/physics.md on citation style (narrative vs parenthetical), equation density, section title voice. Do not import chemistry's taxonomy-monograph habits into a physics RMP draft.

Style guides (one per domain)

Auto-loaded by the agent when <domain> is set. Each is ~1,000–2,000 words of prose distilled from 8–12 landmark reviews in that domain.

DomainVenues mined
physicsRMP, Phys. Rep., Nat. Rev. Phys., Annu. Rev. Cond. Matt., Quantum, Science
chemistryChem. Rev., Chem. Soc. Rev., Nat. Rev. Chem., Science
biologyCancer Discov., Nat. Rev. Genet., Science, eLife, Nat. Struct. Mol. Biol.
medicineNEJM, Nat. Med., Nat. Rev. Drug Discov., Nat. Rev. Genet.
mathematicsSIAM Review, Acta Numerica, Bull. AMS, Probab. Surv., Curr. Dev. Math.
computer_scienceNature, Science, NEJM, IEEE SPM, Nat. Mach. Intell., Annu. Rev. Fluid Mech., Comm. ACM
earth_environmentScience, Rev. Geophys., Nature, Nat. Rev. Earth Env., PNAS, ESSD
astronomyARA&A, Nat. Rev. Phys., A&A, ApJ Letters, PRX, CQG
economicsAnnu. Rev. Econ., JEP, AER, Handbook of Macroeconomics
materialsNature, Nat. Rev. Mater., Science, npj Comput. Mater., Nat. Energy, Chem. Soc. Rev.

References

  • references/anti_patterns.md — the stacking tells (paragraph openers, drive-by citations, "zoo of results" paragraphs, untriaged citation clusters). What to not write.
  • references/synthesis_rubric.md — the 7 canonical moves for unifying disparate work (unifying equation, comparative table, taxonomy tree, contrast pair, timeline, platform comparison, phase diagram).
  • references/transition_moves.md — grammar of section-to-section and paragraph-to-paragraph movement.
  • references/decision_tree.md — venue selection by length + audience + voice.

First-time usage

In a review project:

bash
# 1. Copy the relevant style guide into your project (optional; skill reads
#    from here by default)
cp skills/review/style_guides/physics.md report/style_guide.md

# 2. Draft outline.md FIRST per Step 1 above

# 3. Have PI review the outline — this is the gate

# 4. Draft prose per section, reading the style guide before each section

# 5. Synthesis rewrite pass per Step 3

# 6. Compile

Syncing style guides from source

Style guides are mined from real corpus text in the sister repo review_style_skills. To refresh:

bash
bash skills/review/scripts/sync_style_guides.sh

This re-copies data/style_guides/*.md from ~/Documents/review_style_skills into skills/review/style_guides/.

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 Review AI skill do?

Write domain-authentic review articles that synthesize rather than stack. Covers 10 scientific domains (physics, chemistry, biology, medicine, mathematics, computer_science, earth_environment, astronomy, economics, materials) with per-domain style guides mined from landmark reviews in RMP / Nat. Rev. X / Annu. Rev. / Chem. Rev. / Bull. AMS / etc. Use this skill when writing a survey / review / report that spans multiple primary papers.

Why use Review on TypingMind?

Because you install it once and use it with any model. Review 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 Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Muuuun/luxas/tree/main/skills/review. 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 Review?

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 Review?

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

Is the Review AI skill free?

Yes. It is published on GitHub by Muuuun 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.

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