Agent skill

Scholarly Writing Refiner

by zebbern in zebbern/claude-code-guide

Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure.

MITAuto-check passedResearch & Science

Install Scholarly Writing Refiner

skills CLI
$ npx skills add zebbern/claude-code-guide --skill scholarly-writing-refiner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zebbern/claude-code-guide scholarly-writing-refiner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scholarly-writing-refiner .claude/skills/scholarly-writing-refiner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scholarly-writing-refiner
GitHub stars
4.7k
Token cost
~5.3k tokens
SKILL.md length
2,446 words
Files
2
Skills in repo
46
Repo updated
First seen
Licence
MIT

At a glance

Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure.

  • Works in 10 steps: Review Dimensions Overview → Grammar Review Rules → Word Choice Optimization Rules → …
  • Tasks that involve Scientific writing
  • SKILL.md covers Quick Start, 1. Review Dimensions Overview, 2. Grammar Review Rules and 3. Word Choice Optimization…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scholarly Writing Refiner is an agent skill from zebbern/claude-code-guide. Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Outputs revision suggestions alongside polished text. Triggered by phrases like 'polish this paragraph,' 'check the grammar,' 'rewrite in academic English,' or keywords like manuscript editing, SCI polishing, and journal submission editing.

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Research & Science, covering Scientific writing. The repository describes itself as: Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks from beginner to power user! The licence is MIT.

When your agent uses it

  • Tasks that involve Scientific writing

Example prompts

  • “polish this paragraph,”
  • “check the grammar,”
  • “rewrite in academic English,”
  • “/scholarly-writing-refiner”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Review Dimensions Overview
  2. Grammar Review Rules
  3. Word Choice Optimization Rules
  4. Voice Guidelines
  5. Coherence and Cohesion Rules
  6. Sentence Structure Optimization Rules
  7. Section-Specific Polishing Guide
  8. Polishing Output Format
  9. Domain-Specific Notes
  10. Agent Behavior Guide

What it can do on your machine

Read from SKILL.md and the folder at commit 7ff9fbb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scholarly Writing Refiner loads about 5.3k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 2,446 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zebbern/claude-code-guide at commit 7ff9fbb, republished under its MIT licence (© zebbern). 2,446 words, ~5,257 tokens.

Download SKILL.mdSave it as .claude/skills/scholarly-writing-refiner/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
scholarly-writing-refiner
description
Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Outputs revision suggestions alongside polished text. Triggered by phrases like 'polish this paragraph,' 'check the grammar,' 'rewrite in academic English,' or keywords like manuscript editing, SCI polishing, and journal submission editing.
license
MIT

Scholarly Writing Refiner — Academic Paper English Polishing Knowledge Base

Helps users review and polish English papers paragraph by paragraph according to international academic journal standards. Covers grammar correction, academic word choice optimization, voice normalization, coherence strengthening, sentence variety, and more. Outputs revision suggestions along with the polished text.

Quick Start

Users only need to provide:

  1. Text to polish: One or more paragraphs of English paper content
  2. Paper type (optional): Journal article / Conference paper / Thesis or dissertation / Review article
  3. Target journal/field (optional): e.g., Nature, IEEE, AAAI, Medicine, Computer Science, etc.
  4. Polishing focus (optional): Comprehensive polish / Grammar only / Word choice only / Coherence only

Example:

"Polish this Introduction for me. The target venue is NeurIPS, and I'd like the language to sound more natural with better logical flow."


1. Review Dimensions Overview

Polishing is carried out across 5 dimensions. Each dimension is rated independently with specific revision suggestions:

DimensionLabelReview Focus
GrammarGrammarSubject-verb agreement, tense, articles, prepositions, clause structure, punctuation
Word ChoiceWord ChoiceAcademic register, precision, collocation, avoidance of colloquialisms
VoiceVoice & TenseActive/passive voice selection, tense consistency
CoherenceCoherence & CohesionIntra-paragraph and inter-paragraph transitions, argumentation chain, use of signaling words
Sentence StructureSentence StructureSentence variety, balance of long and short sentences, coordination and subordination

2. Grammar Review Rules

2.1 Common Grammar Errors Checklist
Error TypeIncorrect ExampleCorrectionExplanation
Subject-verb disagreementThe results of the experiment shows...The results of the experiment show...Subject is "results" (plural)
Missing/misused articleWe propose method to solve...We propose a method to solve...Singular countable nouns require an article
Dangling modifierUsing the proposed method, the accuracy was improved.Using the proposed method, we improved the accuracy.The implied subject of a participial phrase must match the main clause subject
Run-on sentenceThe model performs well , it achieves 95% accuracy.The model performs well**;** it achieves 95% accuracy. / The model performs well**. It** achieves 95% accuracy.A comma cannot join two independent clauses
Incomplete comparisonOur method is more efficient.Our method is more efficient than the baseline.Comparatives require an explicit object of comparison
Broken parallelismThe system can detect, classify, and is able to segment...The system can detect, classify, and segment...Coordinated elements must share the same grammatical form
that/which confusionThe model which we proposed...The model that we proposed...Restrictive relative clauses use "that"
Irregular pluralsThese phenomenon indicate...These phenomena indicate...Watch for irregular plural forms
2.2 Punctuation Rules
RuleCorrect UsageCommon Mistake
Serial comma (Oxford comma)A, B**,** and CA, B and C (the Oxford comma is recommended in academic writing)
Em dashWe used three models — A, B, and C — for comparison.An em dash with spaces on both sides, or without spaces (depends on journal style)
Capitalization after colonCapitalize if a complete sentence follows: The result is clear: The model outperforms...Do not capitalize if a fragment follows
Quotation marks and periodsAmerican style: period inside quotes. British style: period outside quotes.Choose based on the target journal's regional convention
Abbreviation periodse.g., i.e., et al., etc.Note the comma: e.g., / i.e.,
2.3 Tense Guidelines by Paper Section
SectionRecommended TenseExample
AbstractPast tense (what was done) + present tense (conclusions)"We proposed a method... The results show that..."
IntroductionPresent tense (current knowledge/consensus) + past tense (prior work)"Deep learning has become... Smith et al. demonstrated that..."
MethodsPast tense (experimental procedures)"We trained the model on... The data were preprocessed..."
ResultsPast tense (experimental findings)"The model achieved 95% accuracy. Table 2 shows..."
DiscussionPresent tense (interpreting significance) + past tense (citing results)"This result suggests that... Our findings indicated that..."
ConclusionPast tense (summarizing work) + present tense (contributions/significance)"We proposed and evaluated... This work contributes to..."

3. Word Choice Optimization Rules

3.1 Colloquial → Academic Substitution Table
ColloquialAcademic AlternativeContext Notes
a lot ofnumerous / a substantial number of / considerableChoose based on what is being modified
getobtain / acquire / achieve / attainChoose based on collocation
showdemonstrate / illustrate / indicate / reveal"demonstrate" emphasizes proof; "indicate" emphasizes suggestion
big / hugesubstantial / significant / considerable
thingfactor / aspect / element / component
goodeffective / favorable / advantageous / robust
badadverse / detrimental / suboptimal / inferior
useemploy / utilize / leverage / adopt"utilize" is more formal than "use"; "leverage" emphasizes exploiting an advantage
aboutapproximately / roughly / circaUse "approximately" for numerical descriptions
tryattempt / endeavor
look atexamine / investigate / analyze / explore
find outdetermine / ascertain / identify / discover
go up / go downincrease / decrease / rise / decline
point outhighlight / emphasize / underscore
deal withaddress / tackle / handle / mitigate
make sureensure / verify / confirm
kind of / sort ofsomewhat / to some extent / partially
start / begininitiate / commence / undertake
end / finishconclude / terminate / complete
helpfacilitate / enable / assist / contribute to
needrequire / necessitate
canis capable of / is able to / has the potential toAvoid over-substitution — "can" is acceptable in academic writing
3.2 Vague → Precise Expression
Vague ExpressionPrecise AlternativeNotes
very good resultsstatistically significant improvement / a 12% increase in accuracyReplace vague modifiers with concrete data
some researchersSeveral studies (Chen et al., 2023; Li et al., 2024)Replace vague references with specific citations
recentlyIn the past five years / Since 2020Provide a time range
a fewthree / a small number of (n=3)Specify the quantity
it is known thatPrior work has established that (citation)Support with a citation
this is importantThis is critical for / This has significant implications forExplain why it matters
3.3 Reducing Redundancy
Redundant ExpressionConcise Version
in order toto
due to the fact thatbecause / since
at the present timecurrently / now
it is worth noting thatNotably, / Note that
it should be pointed out that(state the content directly)
a total of 50 samples50 samples
the vast majority ofmost
in the event thatif
has the ability tocan
on a daily basisdaily
in close proximity tonear
take into considerationconsider
is in agreement withagrees with
serves the function offunctions as

4. Voice Guidelines

4.1 Active vs. Passive Voice Selection
ScenarioRecommended VoiceExample
Describing the authors' actionsActive (We)We trained the model using...
Describing general methods/established factsPassiveThe data were collected from...
Emphasizing the object of an actionPassiveThe samples were analyzed using mass spectrometry.
Reporting resultsPrefer activeOur method achieves 95% accuracy.
Describing equipment/materialsPassiveThe solution was heated to 100°C.
4.2 Common Voice Issues
IssueIncorrect ExampleCorrection
Overuse of passiveIt was found by us that the results were improved by the method.We found that our method improved the results.
Inconsistent personThe author proposes... We then evaluate...Use "We" or "The authors" consistently
Meaningless passiveIt can be seen that accuracy increases.Accuracy increases. / The results show that accuracy increases.
4.3 Academic Person Conventions
PersonUse CaseNotes
WeDescribing the authors' own work (most common)Many journals accept "We" even for single-author papers
The authorsA more formal alternativeSome journals prefer this usage
ISingle-author theses and dissertationsSome journals do not accept this
OneGeneric/hypothetical statementsSomewhat old-fashioned; less common in modern academic writing

5. Coherence and Cohesion Rules

5.1 Intra-Paragraph Signaling Words
Logical RelationshipSignal Words/PhrasesExample
AdditionFurthermore, Moreover, Additionally, In additionFurthermore, our method generalizes well to unseen data.
ContrastHowever, In contrast, Conversely, On the other hand, NeverthelessHowever, this approach suffers from high computational cost.
Cause & EffectTherefore, Consequently, As a result, Hence, ThusTherefore, we adopt a two-stage training strategy.
ExemplificationFor example, For instance, Specifically, In particularSpecifically, we focus on the image classification task.
EmphasisIndeed, Notably, Importantly, It is worth noting thatNotably, the improvement is consistent across all datasets.
ConcessionAlthough, Despite, Notwithstanding, While, Even thoughAlthough the model is simple, it achieves competitive results.
SummaryIn summary, To summarize, Overall, In conclusionOverall, the proposed method outperforms existing baselines.
QualificationYet, Still, Nonetheless, That saidThat said, there are several limitations to our approach.
SequenceFirst, Second, Finally, Subsequently, ThenFirst, we preprocess the data. Subsequently, we train the model.
ConditionIf, Provided that, Given that, Assuming thatGiven that the dataset is imbalanced, we apply oversampling.
5.2 Inter-Paragraph Transition Patterns
PatternDescriptionExample Opening Sentence
HookThe end of one paragraph leads into the next topic"This raises the question of how to efficiently scale the model."
RecapThe next paragraph opens by revisiting the prior conclusion"Having established the effectiveness of our approach, we now turn to..."
Contrast BridgePoints out the shortcomings of the prior approach, introducing the current one"While these methods achieve reasonable accuracy, they fail to address..."
Question BridgeUses a question to create a transition"How can we overcome this limitation? In this section, we propose..."
Topic SentenceThe first sentence of each paragraph summarizes the core argument"The key advantage of our method is its ability to..."
Show full SKILL.md (977 more words)Show less
5.3 Common Coherence Problems
ProblemDescriptionFix Strategy
Jumping argumentationLeaping from A to C without the B stepAdd intermediate reasoning steps or transitional sentences
Signal word overuseStarting every sentence with However / MoreoverReduce signal words; use sentence structure to convey logic
Signal word misuseUsing "Furthermore" to express contrastUse "However" for contrast; "Furthermore" for addition
Overly long paragraphsA single paragraph exceeding 8–10 sentencesSplit into 2–3 paragraphs by argument point
Overly short paragraphsA paragraph with only 1–2 sentencesMerge into a related paragraph or expand the discussion
Unclear reference"This shows..." — what does "this" refer to?"This result shows..." / "This finding indicates..."

6. Sentence Structure Optimization Rules

6.1 Strategies for Sentence Variety
StrategyOriginalImproved
Participial phrase openingWe use attention mechanism, and we improve accuracy.Leveraging the attention mechanism, we improve accuracy.
Inversion for emphasisThe improvement is particularly notable in low-resource settings.Particularly notable is the improvement in low-resource settings.
Appositive insertionThe model, which was proposed by Smith, achieves...The model, proposed by Smith (2023), achieves...
NominalizationWe improved the model, and this led to...The improvement of the model led to...
Parallel structureThe method is fast. It is also accurate. It is scalable too.The method is fast, accurate, and scalable.
Fronted adverbialAccuracy improved significantly when we added data augmentation.With data augmentation, accuracy improved significantly.
6.2 Sentence Structure Problems to Avoid
ProblemExampleFix
Overly long sentences (>40 words)We trained the model on the dataset which was collected from ... and preprocessed using ... and then evaluated on ...Split into 2–3 shorter sentences
Consecutive short sentencesThe accuracy is high. The model is fast. It uses less memory.Combine: The model achieves high accuracy with fast inference and low memory consumption.
Starting with There is/areThere are many studies that focus on...Many studies focus on...
Overuse of It is...that cleft sentencesIt is the attention mechanism that improves...The attention mechanism improves...
Noun pile-upsdeep learning image classification model performancethe performance of a deep learning model for image classification

7. Section-Specific Polishing Guide

7.1 Abstract
  • Length: 150–300 words (follow the target journal's requirements)
  • Structure: Background (1–2 sentences) → Problem/Motivation (1 sentence) → Method (2–3 sentences) → Results (1–2 sentences) → Conclusion/Significance (1 sentence)
  • Tense: Past tense for describing the work, present tense for conclusions
  • Avoid: No citations, no abbreviations without first defining them in full, no figure or table numbers
7.2 Introduction
  • Structure (classic "funnel" approach): Broad context → Specific problem → Existing methods and their limitations → Proposed method/contributions → Paper outline
  • Key points: Each paragraph must have a clear topic sentence; be objective when reviewing prior work — do not disparage
  • Common patterns:
    • "In recent years, ... has attracted increasing attention."
    • "Despite significant progress, ... remains a challenge."
    • "To address this issue, we propose..."
    • "The main contributions of this paper are as follows:"
  • Strategy: Organize by theme (not chronologically); within each group, arrange chronologically
  • Key points: Explain how each work relates to the current paper; avoid mere listing — provide commentary
  • Transitions: Connect each group with transitional sentences
  • Common patterns:
    • "A closely related line of work focuses on..."
    • "In contrast to these approaches, our method..."
    • "Building upon the work of X, we extend..."
7.4 Methods
  • Principle: Reproducibility — the reader should be able to replicate the experiment from the description alone
  • Structure: Problem formulation → Overall framework → Detailed module descriptions → Training/optimization details
  • Key points: Define all mathematical symbols upon first appearance; describe steps in execution order
7.5 Results / Experiments
  • Structure: Experimental setup → Main results → Ablation studies → Analysis/Discussion
  • Key points: Describe trends in text first, then reference tables/figures; avoid repeating numbers already shown in tables or figures
  • Common patterns:
    • "As shown in Table X, our method outperforms..."
    • "We observe a consistent improvement of X% across..."
    • "The ablation study reveals that..."
7.6 Discussion
  • Content: Interpret the significance of results → Compare with prior work → Limitations → Future directions
  • Key points: Do not shy away from limitations; the discussion should go beyond the results themselves and explore broader implications
7.7 Conclusion
  • Length: Typically one paragraph, 150–250 words
  • Structure: Summarize the method → Core findings → Significance/Contributions → Future work
  • Avoid: Do not introduce new information or data; do not simply repeat the Abstract

8. Polishing Output Format

For each paragraph of text provided by the user, output in the following format:

### Original
[User's original text]

### Review

| Dimension | Rating | Key Issues |
|-----------|--------|-----------|
| Grammar | ✓ Good / △ Needs improvement / ✗ Significant issues | Brief description |
| Word Choice | ✓ / △ / ✗ | Brief description |
| Voice | ✓ / △ / ✗ | Brief description |
| Coherence | ✓ / △ / ✗ | Brief description |
| Sentence Structure | ✓ / △ / ✗ | Brief description |

### Detailed Changes
1. **Original**: "..."
   **Revised**: "..."
   **Reason**: [Specific rationale citing the rules above]

2. ...

### Polished Version
[Complete polished paragraph]

9. Domain-Specific Notes

Different disciplines have their own writing conventions. Respect field-specific norms when polishing:

FieldCharacteristicsNotes
Computer ScienceActive voice ("We") is commonMore colloquial phrasing is tolerated (e.g., "we run"); algorithm descriptions must be precise
Medicine/BiologyPassive voice predominates"Patients were randomized..."; terminology must conform to MeSH standards
PhysicsConcise, equation-drivenMathematical derivations must be rigorous; "one can show that..." is common
Social SciencesFrequent use of hedging"may," "might," "suggests"; avoid overly absolute statements
EngineeringResults-orientedEmphasis on performance metrics and experimental validation

10. Agent Behavior Guide

When the user submits text for polishing, follow this workflow:

  1. Confirm details: Paper type, target journal/conference (if any), polishing focus
  2. Identify the section: Determine which part of the paper the text belongs to and apply the corresponding section guidelines
  3. Five-dimension review: Check systematically: Grammar → Word Choice → Voice → Coherence → Sentence Structure
  4. Annotate sentence by sentence: Provide a reason for every change, citing the specific rule
  5. Output the polished version: Deliver the complete polished text
  6. Summarize recommendations: Highlight the main categories of issues and directions for improvement

Core Principles:

  • Preserve the author's original meaning and argumentation logic; do not alter technical content
  • Minimize changes — if a single word can be changed instead of a whole sentence, change only the word; if a sentence can be changed instead of a whole paragraph, change only the sentence
  • When uncertain whether something is an error, present it as a suggestion rather than making the change outright
  • Defer to the author's terminology unless it is clearly incorrect
  • Do not modify content the user has marked as "please keep"

© zebbern, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/scholarly-writing-refiner of zebbern/claude-code-guide.

  • SKILL.md
  • LICENSE

Open the folder on GitHubat commit 7ff9fbb

Compare with similar skills

Scholarly Writing Refiner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scholarly Writing Refiner compared with similar skills
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Citation Verification GuideGalaxy-Dawn/claude-scholar5.7k2 repos~1.9kAutomated safety check: PassMIT
Citation ManagementK-Dense-AI/claude-scientific-writer2.4k2 repos~3.9kAutomated safety check: NotesMIT
Academic Paper Composerlishix520/academic-paper-skills1.4k2 repos~6.3kAutomated safety check: PassMIT
Academic Paper Writing PipelineImbad0202/academic-research-skills51k—~16kAutomated safety check: PassCustom licence

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Questions about Scholarly Writing Refiner

What does Scholarly Writing Refiner do?

Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Scholarly Writing Refiner is an agent skill from zebbern/claude-code-guide. Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure.

When should I use Scholarly Writing Refiner?

Scholarly Writing Refiner fits situations like: tasks that involve Scientific writing.

How do I install Scholarly Writing Refiner in Claude Code?

Run `npx skills add zebbern/claude-code-guide --skill scholarly-writing-refiner -a claude-code`. Or copy the skill folder (skills/scholarly-writing-refiner in zebbern/claude-code-guide) into .claude/skills/scholarly-writing-refiner in your project. Claude Code loads it when a task matches its description.

How do I install Scholarly Writing Refiner in Codex?

Run `npx skills add zebbern/claude-code-guide --skill scholarly-writing-refiner -a codex`. Or copy the skill folder (skills/scholarly-writing-refiner in zebbern/claude-code-guide) into .agents/skills/scholarly-writing-refiner in your project. Codex loads it when a task matches its description.

Can I use Scholarly Writing Refiner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zebbern/claude-code-guide --skill scholarly-writing-refiner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scholarly-writing-refiner, .gemini/skills/scholarly-writing-refiner, .github/skills/scholarly-writing-refiner and .opencode/skills/scholarly-writing-refiner in your project.

What does Scholarly Writing Refiner need to run?

SKILL.md names no scripts, command-line tools or credentials: Scholarly Writing Refiner is instructions for the agent only.

Does Scholarly Writing Refiner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scholarly Writing Refiner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scholarly Writing Refiner use?

Scholarly Writing Refiner is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scholarly Writing Refiner use?

About 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scholarly Writing Refiner?

Skills that share tags, products or a category with Scholarly Writing Refiner: Nature-Style Scientific Figures (Yuan1z0825/nature-skills, 47k stars), Citation Verification Guide (Galaxy-Dawn/claude-scholar, 5.7k stars), Citation Management (K-Dense-AI/claude-scientific-writer, 2.4k stars) and Academic Paper Composer (lishix520/academic-paper-skills, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scholarly Writing Refiner?

zebbern (a GitHub user) maintains it in zebbern/claude-code-guide, which has 4,650 GitHub stars. The repository holds 46 skills in this directory. The repository was last updated on October 10, 2026.

Source: zebbern/claude-code-guide on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.