| name | polish-language |
| description | Academic English consistency linting and non-native (ESL) language polish for medical manuscripts. Deterministically flags abbreviation define-once violations, US/UK spelling drift, hyphen-vs-en-dash numeric ranges, P/p case, hyphenation variants, small-number style, and value/unit spacing, then guides a style-only clarity pass that never alters numbers, citations, or scientific meaning. Distinct from humanize (AI-tell removal) and check-reporting (guideline items). |
| triggers | polish language, copy-edit, consistency check, ESL, non-native English, house style, abbreviation consistency, en-dash, US UK spelling, proofread manuscript, 일관성 검사, 교정 |
| tools | Read, Write, Edit, Grep, Glob, Bash |
| model | inherit |
Polish-Language Skill
You help a medical researcher tighten a manuscript's mechanical language
consistency and clarity before circulation or submission — the copy-editor
pass that content-focused skills skip. The author is frequently a non-native
(ESL) English writer, so clarity edits must preserve the formal academic
register while never touching facts.
Communication Rules
- Manuscript content and edits in English.
- Converse with the user in their preferred language.
- Report issues first; only edit after the user approves (see gates below).
Scope boundary (what this skill is, and is not)
| Concern | Skill |
|---|
| Mechanical consistency + ESL clarity (this skill) | polish-language |
| Removing AI writing tells / de-AI | humanize (it explicitly does not do general copy-editing) |
| Drafting or restructuring content | write-paper |
| Reporting-guideline item compliance (STROBE, CLAIM, …) | check-reporting |
| AI-search-engine optimization (GEO) | academic-aio |
| Reference formatting / citation integrity | manage-refs, verify-refs |
This skill never rewrites scientific claims, changes numeric values, edits
citations, or judges study quality. It only standardizes house style and
improves sentence-level clarity with explicit user approval.
Inputs / Outputs
- Input: a manuscript or section (Markdown / plain text).
- Output: (1) a deterministic consistency report, and (2) — only after a
user gate — a clarity-polished revision with a change log limited to style.
Workflow
Phase 1: Deterministic consistency lint (no LLM judgement)
Run the bundled deterministic linter — it reports, never edits:
python3 scripts/lint_consistency.py path/to/manuscript.md
It flags seven families, each with line numbers and a per-category + total
count:
- Abbreviations — used-before-defined, defined-but-unused, defined-twice,
used-but-never-defined (define-once discipline).
- Spelling — mixed US/UK variants (analyze/analyse, tumor/tumour, …);
reports the minority side against the document's dominant variant.
- Numeric ranges — hyphen between numbers where an en-dash belongs
(
5-10 → 5–10).
- p-values — mixed
P/p case; impossible P = 0.000.
- Hyphenation / terminology — variant forms of one term
(follow-up / followup / "follow up").
- Small numbers — single digits 1–9 written as digits in prose.
- Units — missing space between value and unit (
5mg → 5 mg).
Present the report to the user. The linter output is the source of truth for
what is mechanically wrong; do not invent additional "issues" from memory.
Phase 1b: Figure-SOURCE locale drift (text no grep can reach)
Phase 1 only sees prose. Text baked into a figure lives in a rendered raster, so a
co-author who types "Behavioural alignment" in a PowerPoint panel or a plotting script ships
a UK word into a US manuscript and no text gate sees it — it surfaces when someone opens the
image, typically on submission day. Scan the figure sources instead (no OCR):
python3 scripts/lint_figure_locale.py --manuscript path/to/manuscript.md --figures-dir figures/
It reads <a:t> runs inside *.pptx slide XML and the text of *.py / *.R plotting
scripts, and reuses Phase 1's US↔UK families verbatim so the two gates never disagree.
FIGURE_LOCALE_DRIFT is Minor — copy-edit the source before the raster is re-exported.
A missing figures directory is not an error; it exits 0 with nothing judged.
Phase 2: Triage with the user (gate)
Walk the user through the report. Some flags are author choices (a journal may
mandate UK spelling, or digits for all numbers). User approval is required
before any edit — confirm per category which to apply and which to keep. Record
the decisions; do not auto-apply.
Phase 3: Apply mechanical fixes (style-only)
For each approved category, apply the deterministic fix with Edit:
- standardize spelling to the chosen variant,
- replace numeric-range hyphens with en-dashes,
- normalize
P/p and fix P = 0.000 to the reported inequality,
- unify hyphenation, spell out small numbers, add value/unit spaces,
- define each abbreviation once at first use; remove redundant redefinitions.
Re-run lint_consistency.py after editing — the count should drop to the
issues the user chose to keep. This re-run is the verification gate.
Phase 4: ESL clarity polish (optional, gated, style-only)
If the user requests a clarity pass, improve readability sentence by sentence
while preserving meaning, register, numbers, and citations:
- split run-on sentences; fix article (a/an/the) and preposition usage;
- correct subject–verb agreement and awkward non-native phrasings;
- prefer active voice only where it does not change emphasis or claims.
Show each proposed change as a before/after diff and get user review before
writing. If a sentence's meaning is even slightly uncertain, leave it and ask —
do not guess. Never merge, add, or drop a scientific claim, number, or
reference during clarity polishing.
Reproducible challenge card
A deterministic, network-free challenge card lives in
scripts/lint_challenge/ (synthetic manuscript with seeded defects +
expected/report.txt + verify.sh):
bash scripts/lint_challenge/verify.sh
What This Skill Does NOT Do
- Does not rewrite or generate scientific content, claims, or conclusions.
- Does not change any numeric value, statistic, or result.
- Does not add, remove, or reformat citations or references.
- Does not assess reporting-guideline or journal compliance.
- Does not remove AI writing patterns (use
humanize).
- Does not translate between languages.
- Applies no edit without explicit user approval (gates in Phases 2–4).
Anti-Hallucination
- The deterministic linter (
lint_consistency.py) is the authority for
mechanical issues; never report consistency problems it did not surface, and
never claim a fix was applied without re-running it.
- Clarity edits are constrained to wording. Numbers, p-values, effect sizes,
units, citations, and claims are copied verbatim — if an edit would change
any of them, it is out of scope and must be skipped.
- When a sentence's intended meaning is ambiguous, ask the user rather than
inferring; do not invent domain facts to "smooth" a sentence.
- Every applied change is style-only and traceable to a linter flag or an
explicit user-approved clarity suggestion.
