content-proofreading

name	content-proofreading
description	An academic proofreading skill for Chinese/English manuscripts, triggered when you need automated checks for spelling, grammar, terminology consistency, and formatting before submission.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

• You are preparing an academic paper for journal/conference submission and need a final language + formatting pass.
• You have bilingual (Chinese/English) content and want consistent punctuation, wording, and style across both languages.
• Your manuscript contains domain terminology (e.g., life sciences) and you need consistent Chinese–English term mapping and abbreviation rules.
• You need to validate references, numbers/units, and heading levels against a required style (APA/MLA/GB/T 7714).
• You want a shareable report (HTML or Markdown annotations) with precise error locations and revision suggestions.

Agent Workflow

Follow these steps in order when the user provides text for proofreading:

Step 1: Identify Input Source

• Determine if the user pasted text directly, provided a file path, or attached a .docx/.md file.
• If a file path is given, read the file content. If a .docx file, use word_converter.py to extract text first.
• If the user provided only text inline, use that text directly.

Step 2: Determine Language Scope

• Check if the content is English, Chinese, or bilingual (both).
• Set language parameter accordingly: en, zh, or both.
• If the user did not specify, auto-detect from content.

Step 3: Run English Checks (if applicable)

• If English content is detected, call EnglishChecker().check(text) to check:
  • Spelling (US/UK variants)
  • Grammar (agreement, tense, articles)
  • Punctuation (US/UK conventions)
  • Style (redundancy, passive voice)
• Collect all findings with location, type, and suggested fix.

Step 4: Run Chinese Checks (if applicable)

• If Chinese content is detected, call ChineseChecker().check(text) to check:
  • Typo/misused characters
  • Grammar and collocation
  • Chinese vs English punctuation normalization
  • Academic expression optimization
• Collect all findings.

Step 5: Run Terminology Check

• Call TerminologyManager(domain="biology").check(text) to verify:
  • Bidirectional Chinese–English term correspondence
  • Abbreviation rule compliance (full form on first occurrence)
  • Synonym unification to preferred standard terms
• Collect all findings.

Step 6: Generate Report

• Feed all findings to AnnotationGenerator(output_format="html" or "markdown").
• Generate the report showing:
  • Each issue with precise location (line/offset)
  • Issue type (spelling, grammar, terminology, formatting)
  • Suggested fix
• Present the report to the user. If the user requested an HTML file, save and return the file path.

Step 7: Validate Output

• Verify all detected issues have location + type + fix.
• Confirm the output format matches the user's request (HTML/Markdown).
• If partial, label clearly as PARTIAL.

Key Features

• English checks

  • Spelling (including US/UK variants)
  • Grammar (agreement, tense, articles, clause structure)
  • Punctuation conventions (US/UK)
  • Style suggestions (redundancy detection, passive voice optimization)

• Chinese checks

  • Typo/misused character detection (dictionary-based)
  • Grammar and collocation checks
  • Chinese vs. English punctuation normalization
  • Academic expression optimization suggestions

• Terminology consistency

  • Domain terminology database (life sciences by default)
  • Bidirectional Chinese–English correspondence checks
  • Abbreviation rules (require full form on first occurrence)
  • Synonym unification to preferred standard terms

• Formatting checks

  • Reference style validation (APA/MLA/GB/T 7714, etc.)
  • Number and unit normalization
  • Heading level consistency
  • Abbreviation consistency across the document

• Reporting

  • HTML interactive report or Markdown annotations
  • Precise error localization
  • Actionable revision suggestions

Dependencies

• Python: >= 3.8

• Python packages (install via pip install -r requirements.txt)

  • languagetool-python (version: see requirements.txt) — English grammar checking
  • opencc (version: see requirements.txt) — Traditional/Simplified Chinese conversion
  • jieba (version: see requirements.txt) — Chinese tokenization
  • pyenchant (version: see requirements.txt) — spelling checks
  • markdown (version: see requirements.txt) — Markdown rendering
  • python-docx (version: see requirements.txt) — .docx reading
  • docx2pdf (version: see requirements.txt) — Word-to-PDF conversion

Example Usage

1) Install

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

pip install -r requirements.txt

2) Run (basic)

python scripts/init_run.py --input <paper_file_path> --output <output_path>

3) Run (advanced)

python scripts/init_run.py \
  --input paper.md \
  --output report.html \
  --lang en \
  --style apa \
  --terminology biology \
  --format html

4) CLI parameters

Parameter	Description	Default
--input	Input file path	Required
--output	Output report path	Generates an HTML report by default
--lang	Language to check (en / zh / both)	both
--style	Reference style (apa / mla / gb)	apa
--terminology	Domain terminology set	biology
--format	Output format (html / markdown)	html
--no-pdf	Skip PDF generation during Word→PDF conversion	false

5) Use as a Python module (end-to-end)

from scripts.english_checker import EnglishChecker
from scripts.chinese_checker import ChineseChecker
from scripts.terminology_manager import TerminologyManager
from scripts.annotation_generator import AnnotationGenerator

text = """
Messenger RNA (mRNA) is transcribed in the nucleus.
"""

en_checker = EnglishChecker()
zh_checker = ChineseChecker()
term_manager = TerminologyManager(domain="biology")

results = []
results.extend(en_checker.check(text))
results.extend(zh_checker.check(text))
results.extend(term_manager.check(text))

generator = AnnotationGenerator(output_format="html")
report = generator.generate(results)

with open("report.html", "w", encoding="utf-8") as f:
    f.write(report)

Implementation Details

Architecture / Core Modules

• english_checker.py

  • Core engine for English spelling/grammar/style checks.
  • Designed to be rule-extensible (add or register new rule sets).

• chinese_checker.py

  • Core engine for Chinese typo/grammar/style checks.
  • Includes a library of common academic writing error patterns.

• terminology_manager.py

  • Terminology database management (import/export/query/update).
  • Performs term consistency checks, bilingual mapping validation, and abbreviation policy checks.

• annotation_generator.py

  • Converts detected issues into a visual report (HTML) or annotated Markdown.
  • Ensures issues include location, type, and suggested fix.

• word_converter.py

  • Extracts text from .docx.
  • Optionally converts Word to PDF (can be disabled via --no-pdf).

Terminology database format (JSON)

Organized by domain; each entry can include bilingual forms and abbreviation metadata:

{
  "biology": {
    "cell": {
      "en": "cell",
      "abbrev": null,
      "full_form": null
    },
    "mrna": {
      "en": "mRNA",
      "abbrev": "mRNA",
      "full_form": "messenger RNA"
    }
  }
}

Checking logic (typical):

• If an abbreviation (e.g., mRNA) appears, verify the full form appears at first mention (e.g., messenger RNA (mRNA)).
• If both Chinese and English terms appear, verify they match the configured mapping for the selected domain.
• If synonyms are detected, prefer the standardized term defined in the database.

Rule database format (JSON)

Rules are grouped by language and category:

{
  "english": {
    "spelling": [],
    "grammar": [],
    "style": []
  },
  "format": {
    "references": [],
    "numbers": [],
    "units": []
  }
}

How rules are applied (high level):

• Load rule sets by --lang and --style.
• Run language-specific checks (English/Chinese) and formatting checks.
• Merge results into a unified issue list.
• Render issues into the selected output format (html / markdown) with location-aware annotations.

Extensibility

• Add new rules

  1. Create a rule file under assets/rules/.
  2. Implement rules following the project’s rule template.
  3. Register the rule set in the rule index.
  4. Run tests to validate precision/recall and avoid false positives.

• Add new terminology sets

  1. Create a terminology JSON under assets/terminology/.
  2. Follow the domain structure shown above.
  3. Register the new domain in the terminology index so it can be selected via --terminology.

When Not to Use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

Field	Required	Format/Source	Example	If Missing
User task description	Yes	Text	Research question, writing goal, analysis objective	Stop and ask user to provide
Primary input material	Depends on task	Text, file path, ID, table, or literature	PMID, PDF, CSV, DOCX, keywords, etc.	Specify which material type is missing
Output preference	No	Text	Language, format, target journal, template	Use skill default format

Output Contract

• Primary output: Structured result or target file aligned with this skill's objective.
• Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
• Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
• If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

• Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
• Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
• Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

• Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
• Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Input Validation

This skill accepts requests that match the documented purpose of content-proofreading and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

content-proofreading only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Quick Validation

• Check that key scripts, templates, or reference file paths this skill depends on exist.
• Check that the final output contains the core fields, sections, or files specified for this task.
• Check that results clearly mark assumptions, limitations, and incomplete items.