translation-files/skills/translation-review/SKILL.md

name, description

name	description
translation-review	Review Chinese-English translations for quality issues - terminology, grammar, consistency, formatting. Two workflows - CSV/XLSX batch review (write .dj suggestions) and .dj comparison line-by-line review (surgical patching).

Translation Review

Two workflows, used depending on input format.

Workflow A: CSV/XLSX batch review

Use when input is a CSV/XLSX with Chinese/English columns. Produces an edit-suggestions.dj file.

1. Get the data into CSV

If XLSX, export to CSV (or use openpyxl). CSV is faster.

2. Read the full file

Use read_file with offsets for complete coverage. Don't sample.

3. Write a systematic analysis script

Write to /tmp/script.py, run with python3 /tmp/script.py. No heredocs or -c. The script should:

• Parse CSV with csv.DictReader
• Apply detection rules per category
• Collect issues: row number, CN text, EN text, problem, suggested fix
• Group/deduplicate identical issues

Common detection categories:

• Buddhist terminology: 正念→mindfulness (not "righteous thoughts"), 布施→generosity (not "alms")
• Identity terms: 学士/修士/胜士/智士 are practice stages, not titles
• Literal machine translations: "hard drive" for 硬盘 (endurance)
• 四摄法 terms: 同事→"acting in harmony", 爱语→"kind speech"
• Grammar: subject-verb agreement, unbalanced quotes
• Typos/formatting: Chinese punctuation in English, "IOS"→"iOS"
• Inconsistency: same CN term translated differently across rows

4. Write edit-suggestions.dj

Format:

# 1

original: <Chinese text or key term>
translated: <current English>

<Explanation and suggested fix.>

# 2
...

One entry per problem category, not per row. Mention affected row numbers.

Workflow B: .dj comparison file review

Use when input is a .dj comparison file (Chinese/English alternating line pairs).

Two modes — always clarify which one

AGENTS.md defines two workflows. Before starting, determine which mode you're in:

1. Translation review (Workflow A in AGENTS.md): agent translated the text. Authoritative target.dj does not exist yet. Review everything: terminology, grammar, formatting, em-dashes, consistency, calques, missing content. Produces translation-findings.dj and applies patches.

2. Proofread (Workflow B in AGENTS.md): English comes from an existing DOCX manuscript. It is authoritative. Only flag manuscript-level mechanical issues: typos, double words, numbering mismatches, garbled text, duplicate text. Produces edit-suggestions.dj ONLY — do NOT apply patches without asking. Do NOT flag: terminology choices, djot formatting (em-dashes, italics), translation style, calques, word order. These are translation-review concerns.

1. Read the full file

Use terminal: cat — read_file deduplicates within a session.

2. Scan for problems (ordered by severity)

Sanity checks first (mechanical, no judgment needed):

• Line count: source and target must match exactly. Mismatch means paragraphs were dropped, merged, or split.
• Em-dash convention: AGENTS.md says English em-dash (—) → three hyphens (---). The Chinese source often uses ------ (six hyphens) as its em-dash equivalent — convert to --- in target, not to a Unicode —. A find/replace — → --- over the target file catches all instances at once; a typical long file has 30–50.
• TOC format: AGENTS.md says TOC must be a plain bullet list, no link targets. If target still has [I. Heading](#...) markdown links, strip them. Also check source TOC — per MPI conventions, both source and target should use clean bullet format.

Terms database drift (systematic):

• Cross-reference glossary terms against the MPI terms database
• CLI preferred: python3 /home/user/documents/mpi/terms-search/search.py <query>. For a review, batch many queries in one execute_code script (subprocess loop) — one terminal call per term is slow and noisy.
• Source priority: DoT定稿 > 内部特色词 > 佛教术语 > 经论名
• Fix both glossary comments AND body text
• See references/terms-db-alignment.md for batch-lookup patterns

Real errors (affect meaning):

• Mistranslation of key terms
• Garbled/malformed source text
• Wrong proper names or technical terms

Inconsistency (confusing but not wrong):

• Terminology drift across file
• Numbering style chaos
• Grammatical voice/person shifts

Cleanup needed:

• Processing artifacts (HTML comments, markers)
• Stray spacing in Chinese text
• Awkward line splits
• Odd word choices
• Redundant English calques: when the target mirrors a Chinese grammar pattern literally, it can read as a typo (e.g. "mind of death-mindfulness" for 念死之心 — should be "mindfulness of death").
• Clunky idioms: 一念之差 → "a single thought of difference" is unidiomatic. Standard renderings exist (e.g. "a single errant thought", "a moment's carelessness", or rephrase as "a single thought can make all the difference").

Missing content:

• Bare headings with no body — flag, don't invent.
• Mid-paragraph truncation (common in MPI translations): CN paragraph covers 3–5 clauses but EN stops after 1–2 sentences. Detection: compare semantic density, not character count. CN often packs more meaning per character than EN. Signal: CN has quoted speech, poems, multiple examples, or a rhetorical climax that's absent from EN. Flag as "Missing Content" not "Incomplete" — these are usually draft-stage cutoffs, not intentional omissions.

3. Dump findings to translation-findings.dj

Finding N — Title (line numbers)
  Chinese: ...
  English: ...
  Issue: description

4. Apply fixes with patch

Surgical string replacement. Verify every patch with cat — never rely on read_file (session dedup).

5. Final sweep

Run python3 scripts/sweep.py <source.dj> <target.dj> [--stale term1,term2] [--new term1,term2]. This runs all mechanical checks in one call: line parity, heading parity, Unicode em/en-dashes, Markdown bold, Chinese punctuation, TOC link artifacts, unbalanced quotes, and stale/new term assertions. Run even when no content patches were needed — it serves as final validation.

Buddhist terminology reference

See references/buddhist-terminology.md for Chinese-English term mappings and common pitfalls.

Workflow C: Typeset proofread (DOCX manuscript vs PDF layout)

Use when the user gives a manuscript DOCX and a typeset PDF and asks to proofread. Goal: catch typesetting errors (missing text, typos, wrong special characters, bad line breaks), not translation quality.

0. Clarify scope FIRST

Before any extraction: ask what they want checked. "Proofread" can mean:

• Text accuracy (missing/doubled words, typos introduced by typesetter)
• Special characters (quotes, dashes, ellipses)
• Formatting (page numbers, headers, TOC layout)
• All of the above

Do not run extraction pipelines until scope is clear.

1. Extract text

• DOCX → plain: pandoc file.docx -f docx -t plain --wrap=none
• PDF → plain: pdftotext -layout file.pdf (preserves positional info)

2. Clean PDF artifacts

• Strip InDesign slug lines, page headers, page numbers
• Join hyphenated line breaks (line ending - + next line starting lowercase)
• Fix drop-cap artifacts (e.g. L iving → Living)

3. Compare

• Extract English paragraphs from DOCX (skip Chinese lines, match blank-line pattern)
• Check each DOCX paragraph exists as substring in PDF body text
• Flag paragraphs not found; investigate each (may be heading renumbering, not missing)

Pitfalls specific to this workflow

• PDF paragraph joining is lossy — page breaks split paragraphs. Don't expect perfect paragraph matching; check content coverage, not paragraph identity.
• Heading numbering differs — DOCX has 1., (1); PDF has I, 1). Ignore heading-only differences.
• InDesign PDFs insert extra spaces around drop caps and special characters. Normalize multi-space to single space before comparison.

Pitfalls

• Clarify scope before diving into extraction pipelines — if the user says "proofread this" or "校对这篇文章", ask what specifically they want checked before running pandoc/pdftotext. Getting interrupted mid-pipeline wastes context.
• Don't use heredocs or -c — write to /tmp/script.py first
• Deduplicate aggressively — group by problem type, not per-row
• Buddhist terminology is technical — don't guess. When uncertain, flag for review
• Never delete .dj comparison files — intentional work artifacts
• Verify patches with cat — read_file dedup makes it unreliable
• Re-read before fixing — user may have made interim edits
• Em-dash drift (translation mode only): AGENTS.md mandates — (Unicode em-dash) → --- (three hyphens) in English djot for the translation workflow. When proofreading an existing DOCX manuscript, do NOT flag em-dashes — the manuscript's English is authoritative and this is a formatting concern for the translation workflow. If you're in translation mode and the target has Unicode em-dashes, run a single find/replace — → ---. Long files typically have 30–50 such instances.
• Batch terminology lookups — when checking many terms against the terms DB, run them in one execute_code script that loops over a query list and calls search.py via subprocess.run. One terminal call per term floods the context with repetitive output.
• Proofread ≠ translation review — when the user says "校对" or "proofread" and the input is a DOCX manuscript with existing English, you are in proofread mode. Do NOT flag translation quality, terminology, or djot formatting. Do NOT apply patches to bilingual.dj unless asked. Write edit-suggestions.dj with manuscript-level issues only. If the user later asks for translation review of the same article, write findings to a separate translation-findings.dj.

References

• references/buddhist-terminology.md — Chinese-English Buddhist term mappings and pitfalls
• references/terms-db-alignment.md — Batch-aligning glossary terms against the MPI terms database
• references/translation-pitfalls.md — Recurring CN→EN mistranslation patterns (关爱→compassion, 生生增上, etc.)
• references/proofreading-patterns.md — DOCX/PDF extraction techniques, block-based pairing, common manuscript issues

Scripts

• scripts/sweep.py — Mechanical validation sweep for completed reviews
• scripts/review_csv.py — Batch CSV/XLSX translation review