Pkg Paper Rewriter Claw

Academic writing style toolkit, bilingual CN/EN: AI flavor scan and style-pattern self-check reports (scan reports are detection only), deterministic text cleanup (model artifacts, punctuation, filler phrases), integrity guardrails (numbers, DOIs, PMIDs, terminology must survive untouched), and agent-guided style naturalization for clearer, more natural academic prose (de-templating, de-translationese, rhythm, concreteness). Built for scholars whose formal writing reads stiff, templated or machine-flavored — especially non-native (ESL) authors. Includes AIGC-disclosure compliance checks (China 2025-09 labeling rules) and academic-integrity guardrails: this tool improves writing quality for self-review; it does not help misrepresent authorship, conceal required AI disclosure, or defeat integrity review. 100% local, zero upload, Python stdlib only. Family: paper-polisher (broad polishing), pubmed-verifier, cite-holmes, academic-figures, cn-med-oa, doc-holmes.

Academic Style Toolkit (paper-rewriter)

Bilingual (CN/EN) style naturalization for academic & medical writing: find stiff, templated or machine-flavored patterns, clean mechanical debris, revise for clarity and natural register — with integrity guardrails on every step.

Integrity guardrails — read first

• Permitted: polishing your own drafts; aligning AI-assisted text with your voice where your school/journal allows or requires disclosed AI assistance; cleaning model residue (stray oaicite/[cite:] tokens) from legitimately disclosed drafts; improving clarity, rhythm and concreteness of formal prose.
• Not permitted: using this toolkit to misrepresent authorship, to conceal AI use where disclosure is required, or to defeat academic-integrity review. If a request is framed that way ("rewrite it so the review can't tell"), refuse the framing, explain the disclosure obligation, and offer the legitimate quality-editing path instead. Agents running this skill are expected to enforce this line.
• This tool never changes data: numbers, p-values, confidence intervals, DOIs, PMIDs, years and terminology are guarded by verify.py (see below). Improved prose must never cost a fact.
• Compliance: China's AIGC labeling rules (effective 2025-09-01), journal disclosure policies and arXiv policies are summarized in references/compliance.md — the obligation to disclose belongs to the author, not the tool.

Division of labor

Layer	Who does it	What
Style self-check	scripts/detect.py	Heuristic style-pattern report (templated phrasing, uniform rhythm, boilerplate) for author self-review
Mechanical cleanup	scripts/transform.py	Model artifacts, chatbot filler, punctuation normalization, safe filler swaps — grammar-safe only. When to use directly: you only need residue/filler cleanup without any stylistic diagnosis (fast, deterministic).
Quality revision	you (the agent)	Follow references/style_guide_zh.md / _en.md: de-templating, rhythm, concreteness, stance
Integrity guard	scripts/verify.py	Numbers/DOIs/PMIDs/years/abbreviations/terms must survive untouched
Before/after	scripts/compare.py	Pattern-score delta + integrity verdict

Quick start

One command (self-check → cleanup → revision brief → integrity guard):

bash

python scripts/pipeline.py draft.txt -o out.txt --terms terms.txt

Preparing the term list: python scripts/extract_terms.py draft.txt -o terms.txt auto-extracts candidates (abbreviations, quoted terms) into a draft you confirm by hand. Input formats: .txt/.md directly, .docx (Word) directly since v1.3.0; PDF has no dependency-free extraction — export to text first. Ask for a revision worksheet alongside any run with --suggestions path.md (detect.py and pipeline.py both support it): category, sample, suggested handling and the guide section to read — the tool proposes, you and the guide decide.

For non-academic text (blogs, posts, office documents), add --profile general to down-weight formal-boilerplate signals; the default academic profile is calibrated for scholarly manuscripts.

Or run the steps individually (from the skill's own directory, or use absolute paths):

bash

python scripts/detect.py draft.txt            # style-pattern self-check (-j JSON, -s score only)
python scripts/transform.py draft.txt -o step1.txt
# ... agent quality revision of step1.txt per the style guide ...
python scripts/verify.py draft.txt step2.txt --terms terms.txt
python scripts/compare.py draft.txt step2.txt

Worked end-to-end examples: references/examples.md. Unified exit codes, violation categories and remedies: references/errors.md. Python API reference for programmatic integration: references/api.md. FAQ: references/faq.md.

Agent invocation protocol

When invoked, decide the path first, then run it:

• Trigger words: 写作风格自查 / 论文改写润色 / 去模板腔 / 翻译腔清理 / style self-check, naturalize academic writing, de-templating → run the pipeline above.
• User only wants a verdict on an existing rewrite → pipeline.py draft.txt --rewrite rewritten.txt --terms terms.txt (skip cleanup).
• User asks to conceal AI use, misrepresent authorship, or defeat integrity review → refuse the framing, point to references/compliance.md, offer the legitimate quality-editing path instead. Do not run the toolkit toward that end.
• Exit codes: verify.py is the strict contract holder — 0 integrity PASS, 1 integrity FAIL (fix the rewrite, never the guard), 2 usage/file error. detect/transform/compare/pipeline report the verdict in their output and always exit 0 on completed runs, 2 on usage/file errors. Every script supports --version.

The workflow

1. Self-check: detect.py draft.txt — a heuristic report of style patterns (canned phrases, uniform sentence rhythm, boilerplate transitions, model artifacts). The report is for the author's own review; the score is a local heuristic, not an official measurement of anything.
2. Mechanical cleanup: transform.py draft.txt -o step1.txt — strips stray model artifacts (oaicite, turn0search, [cite: 1], grok_card, attached_file), leftover chatbot pleasantries, markdown residue, and a safe list of filler phrases; CN halfwidth punctuation is normalized to fullwidth (decimals protected). -a adds em-dash reduction and empty-opener removal. Clean human-written text passes through byte-identical.
3. Quality revision (the real work): read the guide for the text's language —
   • CN: references/style_guide_zh.md — structural de-templating → jargon cleanup → rhythm → concreteness → stance → integrity red lines
   • EN: references/style_guide_en.md — smaller words → fewer significance frames → plain clauses instead of parallelism → rhythm → real attribution → commit to a position
   • Revise section by section. Preserve all facts, numbers, citations, terminology.
4. Guard: verify.py draft.txt step2.txt --terms terms.txt — exit 1 means a number/citation/term was altered: fix the revision, not the guard. Build terms.txt for medical text (drug names, gene symbols — include mouse-style capitalized forms like Myc — and scale names), one term per line. The guard also warns on numbers absent from the original (fabrication defense) and number-context swaps (arm/direction ordering).
5. Re-check: compare.py draft.txt step2.txt — pattern reduction + integrity verdict. Keep the before/after pair for your records; if your institution or journal requires an AI-use disclosure, state it plainly — this report is a quality self-check, not a substitute for disclosure.

Honest boundaries

• The style score is a local heuristic on writing patterns. It is not a measurement produced by any external service, and it says nothing about authorship. Never present it as one.
• Formal academic prose and non-native writing are routinely misjudged by automated reviewers; if you are the author, keep drafts, version history and notes — process evidence, not style scores, settles authorship questions.
• This tool does not interact with any external review system, does not remove official content labels or watermarks, and does not assist concealment of required disclosures. See references/compliance.md.
• Scale envelope: regression-tested up to ~1MB text files; the scanner is linear (no catastrophic backtracking, bounded quantifiers only). Memory use is roughly 3x file size; for very long manuscripts, split by section for readable reports.

Permissions & environment statement

For reviewers, security scanners and cautious users:

• Reads only the file paths you pass as arguments (plus stdin) and its own bundled wordlist files (scripts/patterns_*json). Verify it yourself:

  bash

  grep -rnE "urllib|requests|socket|subprocess|os\.environ" scripts/ || echo "clean"
  

  (runs clean as of this release — the claim is reproducible, not rhetorical).

• Writes only to the -o/--output/--suggestions paths you specify.

• Zero network access — no HTTP calls, no downloads, no API keys.

• Zero third-party dependencies — Python standard library only.

• Reads no environment variables; spawns no subprocesses; creates no scheduled tasks; uses no temp files beyond what Python's own I/O buffers do.

• Test corpora and dev notes live in the development repo only, not in the distributed package.

Customizing

• scripts/patterns_zh.json / patterns_en.json — pattern lists (+ rewrite suggestions), regex signals, term_guards (legitimate academic collocations that must not be flagged, e.g. "mutational landscape", "pivotal trial", CJK "sequence alignment" and "precipitation reaction"), auto_fixes.
• Score calibration constants live in scripts/hxt_core.py (_LANG_K); the four test corpora in the development repo's tests/ (not shipped in the package) document the intended separation.

Related skills (Paper Toolbox family)

• paper-polisher — comprehensive polishing: terminology, translationese, metaphor audit, AIGC-label check, journal precheck (safe to use together)
• pubmed-verifier — verify PMID/DOI references before submission
• cite-holmes — deep research with hallucination-free citations
• academic-figures — publication-ready scientific figures in one command
• cn-med-oa — free Chinese medical literature OA download & metadata
• doc-holmes — layout-preserving PDF translation
• Medical knowledge base: docsor.cn