translation-quality

name	translation-quality
description	Translation quality check workflow and criteria using xCOMET
version	1.0.0
owner	@shuji-bonji
last_reviewed	"2026-01-28T00:00:00.000Z"

Translation Quality Check Skill

Purpose

Ensure high-quality translations from Japanese to English for documentation and technical content. This skill provides standardized criteria and workflows for evaluating and improving translation quality.

Why This Skill?

• Consistent quality standards across all translated documents
• Automated quality gates before publication
• Clear remediation workflow for low-quality translations

MCP Tools

MCP	Tool	Purpose
xcomet	xcomet_evaluate	Calculate quality score for single pair
xcomet	xcomet_detect_errors	Detect error spans with severity
xcomet	xcomet_batch_evaluate	Evaluate multiple pairs efficiently
deepl	translate-text	Re-translate if quality is poor

Quality Criteria

Score	Rating	Action
0.95+	⭐⭐⭐⭐⭐ Excellent	Ready for publication
0.85-0.94	⭐⭐⭐⭐ Good	Minor review recommended
0.70-0.84	⭐⭐⭐ Acceptable	Review and improve required
< 0.70	⭐⭐ Poor	Re-translation required

Workflow

Step 1: Identify Translation Pairs

Locate source (Japanese) and target (English) document pairs:

docs/skills/overview.ja.md  ←→  docs/skills/overview.md
README.ja.md                ←→  README.md

Step 2: Evaluate Quality

For single documents:

xcomet:xcomet_evaluate
  - source: Japanese text
  - translation: English text
  - source_lang: ja
  - target_lang: en

For multiple documents (5+ pairs):

xcomet:xcomet_batch_evaluate
  - pairs: [{source, translation}, ...]
  - source_lang: ja
  - target_lang: en

Step 3: Analyze Results

If score < 0.85:

xcomet:xcomet_detect_errors
  - source: Japanese text
  - translation: English text
  - min_severity: minor

Step 4: Remediate

Condition	Action
Score < 0.70	Re-translate with deepl:translate-text
Score 0.70-0.84 with errors	Fix specific error spans
Score 0.85+ with minor issues	Optional refinement

Decision Criteria

Condition	Action	Rationale
All scores ≥ 0.95	Approve for publication	Excellent quality
Average ≥ 0.85, no critical errors	Approve with notes	Good enough quality
Any score < 0.70	Block publication	Quality gate failure
Critical errors detected	Manual review required	Semantic issues

Examples

Example 1: Single Document Check

Input:

Source (JA): MCPだけでは不十分
Translation (EN): MCP alone is not enough

Process:

1. Call xcomet_evaluate with source_lang=ja, target_lang=en
2. Score: 0.973 (Excellent)
3. Result: Ready for publication

Example 2: Batch Check

Input:

[
  {"source": "設計思想", "translation": "Design principles"},
  {"source": "実践ノウハウ", "translation": "Practical knowledge"}
]

Process:

1. Call xcomet_batch_evaluate
2. Average: 0.95+
3. Result: All pairs ready

Example 3: Low Quality Detection

Input:

Source (JA): エージェント側でのカスタマイズが困難
Translation (EN): Customization difficult agent side

Process:

1. xcomet_evaluate → Score: 0.65 (Poor)
2. xcomet_detect_errors → Grammar issues detected
3. Re-translate with deepl
4. Re-evaluate → Score: 0.92 (Good)

Anti-Patterns

❌ Skipping Batch Evaluation

Problematic:

# Evaluating 20 pairs one by one
for each pair:
    xcomet_evaluate(pair)  # Inefficient

Correct:

# Use batch for 5+ pairs
xcomet_batch_evaluate(all_pairs)  # Efficient

❌ Ignoring Error Severity

Problematic:

Score is 0.80, no errors shown → Approve

Correct:

Score is 0.80 → Run xcomet_detect_errors
Check for critical/major errors before approval

Related MCPs

MCP	Usage	Required
xcomet	Quality evaluation	Required
deepl	Re-translation	Recommended

References

• xCOMET MCP Server
• DeepL MCP
• Translation Workflow Pattern