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unslop-academic-writing

unslop-academic-writing

github.com/kalarislabsDocuments73109 installsUpdated 3d ago
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About unslop-academic-writing

A writing skill for improving academic prose by identifying vague, inflated language and guiding evidence-specific revisions. It is intended for researchers and authors, with an emphasis on preserving scientific meaning and the author’s voice rather than disguising AI use.

Key features

  1. 01Lint drafts for vague prose
  2. 02Revise claims before wording
  3. 03Preserve numbers and technical terms
  4. 04Match the author’s voice
  5. 05Recheck and read revisions aloud

Use cases

  1. 01Editing a manuscript introduction
  2. 02Reducing generic academic phrasing
  3. 03Revising prose without changing claim strength

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/kalarislabs/research-agent-skills --skill unslop-academic-writing

After installing, type/unslop-academic-writingin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
discipline-voice.md
patterns.md
scripts/
slop_check.py

Details

From the project README

Unslop Academic Writing

Slop is prose that is fluent but says less than it seems to. Readers, reviewers and editors now recognize it on sight, and it costs credibility even when the science is sound. The fix is specificity: every sentence should carry information a generic paper on the topic would not.

This skill is about clarity and authorship, not about disguising AI use. Follow the venue's policy on AI assistance (most require disclosure, see reproducibility-statement). The author remains responsible for every claim.

Workflow

  1. Measure. Run the linter on the draft (or a section):

    python scripts/slop_check.py manuscript.tex
python scripts/slop_check.py intro.md --json > slop.json

    It ignores math, citations, code and markup. Index bands: < 5 clean, 5-15 some slop, 15-30 heavy, > 30 severe. Each finding has a line number and a rewrite hint.

  2. Fix content first, words last. Work top-down. Polishing vocabulary in a paragraph that should not exist is wasted effort.

    LevelQuestionTypical fix
    ClaimDoes each paragraph make one claim a specialist would find non-obvious?Cut paragraphs that restate the field; merge duplicates
    EvidenceIs every claim tied to a number, citation, figure or method?Replace "significantly improves" with the effect size, interval and test
    StructureDoes the logic carry the connection, or do signposts ("Moreover") fake it?Reorder sentences so each follows from the last; delete connective filler
    RhythmDo sentence lengths vary with content?Short sentence for the key finding; longer ones for mechanism and caveats
    WordsStock vocabulary, hype, stacked hedges?Use the hints and references/patterns.md

  3. Preserve what matters. Never change numbers, units, citations, technical terms, defined abbreviations or the strength of a claim while "unslopping". Hedges that reflect genuine uncertainty stay. Remove only the second hedge in a stack.

  4. Match the author's voice. Ask for 1-3 pages the author wrote before using AI (a previous paper or thesis chapter). Note their sentence length, use of "we"/"I", preferred transitions and terminology, then keep the revision within that range. Follow the discipline norms in references/discipline-voice.md.

  5. Re-measure and read aloud. Target an index under 5 for abstracts and introductions, under 10 for methods-heavy sections. The linter is a floor, not a judge: finish by reading the text as a skeptical reviewer.

Rewrite rules (apply in order)

  1. Delete the frame, keep the point. "It is important to note that X" → "X".
  2. Replace abstraction with the specific thing. "a wide range of tasks" → "12 of the 14 benchmark tasks".
  3. Replace evaluation with evidence. "a remarkable improvement" → "a 9.4-point improvement (95% CI 7.1-11.7)".
  4. Name the mechanism, not the role. "plays a pivotal role in" → "is required for", "accounts for 40% of".
  5. One hedge, sized to the evidence. "may potentially suggest" → "suggests" (strong data) or "is consistent with" (weak data).
  6. State the contrast directly. "not only X, but also Y" → "X and Y", or keep only the stronger point.
  7. Use the verb. "perform an analysis of" → "analyse"; "provide a description" → "describe".
  8. Let structure replace signposts. Delete "Moreover/Furthermore/Additionally" where the logical relation is clear.
  9. Punctuate normally. Most em dashes become commas, colons, parentheses or full stops.
  10. Vary rhythm deliberately. After two long sentences, write a short one that states the takeaway.

Before and after

Before: In today's rapidly evolving landscape, single-cell technologies play a pivotal role in unraveling the intricate tapestry of cellular heterogeneity. It is important to note that our novel framework significantly outperforms existing methods, paving the way for groundbreaking discoveries.

After: Single-cell RNA-seq atlases now exceed 10 million cells, but cluster labels still disagree between annotation tools on about 20% of cells. Our method reduced this disagreement to 7% on three held-out atlases (paired t-test, p < 0.01), mainly by resolving T-cell subtypes that marker-based tools merge.

Integrity rules

  • Never fabricate specifics to replace vague text. If the draft lacks the number or citation, ask the author or mark a placeholder such as [N = ?].
  • Verify that each rewritten sentence says the same thing as the original, at the same strength.

What the linter cannot judge

Accuracy, novelty, whether a citation supports the sentence, or whether the argument is right. Pair this skill with citation-verification, scientific-critical-thinking and a human co-author's read.

Related skills

scientific-writing, ml-paper-writing, abstract-and-title, rebuttal-and-response-to-reviewers, cover-letter-to-editor, peer-review, reproducibility-statement.

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