Agent skill

Multilingual Score

by indranilbanerjee in indranilbanerjee/digital-marketing-pro

Score translated content for publication: quality, brand voice, market compliance.

MITAuto-check passedWriting & Content

Install Multilingual Score

skills CLI
$ npx skills add indranilbanerjee/digital-marketing-pro --skill multilingual-score -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install indranilbanerjee/digital-marketing-pro multilingual-score --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/multilingual-score .claude/skills/multilingual-score && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
multilingual-score
GitHub stars
862
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
1,487 words
Files
1
Skills in repo
162
Repo updated
First seen
Licence
MIT

At a glance

Score translated content for publication: quality, brand voice, market compliance.

  • Works in 8 steps: Load brand context: Read… → Run technical translation scoring:… → Run content quality evaluation: Execute… → …
  • Tasks that involve Translation
  • SKILL.md covers Purpose, Input Required, Process and Output, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Multilingual Score is an agent skill from indranilbanerjee/digital-marketing-pro. Score translated content for publication: quality, brand voice, market compliance. "is this German translation ready to publish"

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Writing & Content, covering Translation, Brand voice and tone and Data visualization. The repository describes itself as: An open-source AI marketing operating system for strategy, SEO, AEO/GEO, paid media, content, CRM, and analytics - grounded in brand context, human approval, and verifiable… The licence is MIT.

When your agent uses it

  • Tasks that involve Translation
  • Tasks that involve Brand voice and tone
  • Tasks that involve Data visualization

Example prompts

  • “is this German translation ready to publish”
  • “/multilingual-score”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load…
  2. Run technical translation scoring: Execute language-router.py --action score with the original content, translated content, source…
  3. Run content quality evaluation: Execute eval-runner.py --action run-quick on the translated content alone, scoring it as standalone…
  4. Run brand voice consistency check: Execute brand-voice-scorer.py --brand {slug} --text "{translated_content}" to score how well the…
  5. Check compliance for target market: Based on the target language-region code, identify applicable regulatory requirements from…
  6. Compute multilingual composite score: Calculate the weighted composite score across all four dimensions — translation technical score (40%…
  7. Classify the result: Based on the composite score, assign a clear action classification — 85 and above: Publish Ready (content meets…
  8. Generate improvement suggestions: For any dimension scoring below its threshold (technical < 85, content quality < 80, brand voice < 75…

What it can do on your machine

Read from SKILL.md and the folder at commit 9e949f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Multilingual Score loads about 3k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 1,487 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from indranilbanerjee/digital-marketing-pro at commit 9e949f3, republished under its MIT licence (© indranilbanerjee). 1,487 words, ~2,972 tokens.

Download SKILL.mdSave it as .claude/skills/multilingual-score/SKILL.md (or your agent's skills folder).
name
multilingual-score
description
Score translated content for publication: quality, brand voice, market compliance. "is this German translation ready to publish"

/digital-marketing-pro:multilingual-score

Purpose

Score translated or localized content across multiple quality dimensions to determine whether it is ready for publishing, needs native speaker review, or requires re-translation. Combines technical translation scoring (length ratios, formatting preservation, placeholder integrity, key term consistency) with content quality evaluation, brand voice consistency checking, and market-specific compliance verification into a single composite multilingual quality score.

Use this command after any translation or localization workflow to validate quality before content goes live. It replaces subjective "looks good" assessments with a structured, repeatable scoring methodology that catches issues automated translation often introduces — brand voice drift, formatting damage, missing do-not-translate terms, compliance gaps in the target market, and length distortion that signals missing or added content. The composite score provides a clear publish/review/re-translate classification so the team knows exactly what action to take.

Input Required

The user must provide (or will be prompted for):

  • Original content: The source text that was translated — provided as inline text, a file path, or a URL to the source content. This serves as the reference for translation accuracy scoring. Required for technical translation scoring; if omitted, only content quality, brand voice, and compliance dimensions are scored
  • Translated content: The translated or localized text to score — provided as inline text, a file path, or a URL. This is the primary content being evaluated. Required
  • Source language: The language code of the original content (e.g., en-US, en-GB, de-DE). Defaults to the brand's primary language from the language configuration if not specified
  • Target language: The language code of the translated content (e.g., de-DE, fr-FR, hi-IN, ja-JP). Required — determines which compliance rules apply and which translation service benchmarks to reference
  • Do-not-translate terms (optional): Specific terms that must appear unchanged in the translation. Defaults to the brand profile's language.do_not_translate list. Additional terms can be provided to supplement the brand list for this specific scoring run
  • Content type (optional): The type of content being scored — blog, email, ad, landing_page, social, product_description, legal, technical. Affects content quality scoring weights and brand voice expectations. Defaults to auto-detection based on content structure

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice dimensions, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Load the language configuration — specifically the do-not-translate terms from language.do_not_translate and any translation quality baselines from past scoring runs. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and voice-and-tone rules that apply across all languages. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Run technical translation scoring: Execute language-router.py --action score with the original content, translated content, source language, target language, and do-not-translate terms. This produces four sub-scores: length ratio (translated content length versus expected length for the language pair — e.g., German typically runs 10-35% longer than English, Japanese typically runs 20-50% shorter, per the EXPANSION_RATIOS table in language-router.py (the source of truth for expected ranges); deviations beyond expected ranges indicate missing or added content), formatting preservation (markdown structure, HTML tags, merge tags like {{first_name}}, UTM parameters, and link structures survived translation intact), key term consistency (every do-not-translate term from the brand profile and any additional specified terms appears exactly as specified in the translation), and placeholder integrity (all variables, template tokens, and dynamic content markers are present and correctly positioned in the translated version).
  3. Run content quality evaluation: Execute eval-runner.py --action run-quick on the translated content alone, scoring it as standalone content in the target language. This assesses structural quality, readability for the target audience, completeness, and coherence — catching cases where a translation is technically accurate but reads poorly as native content. The eval-runner scores content against the plugin's standard quality dimensions regardless of whether it was translated or originally authored.
  4. Run brand voice consistency check: Execute brand-voice-scorer.py --brand {slug} --text "{translated_content}" to score how well the translated content matches the brand's voice profile. Brand voice should survive translation — the brand should sound recognizably like itself in every language, adapted for local expectations but maintaining its core personality dimensions (formality, energy, humor, authority). Score the translated content against the same voice dimensions as the original to detect voice drift introduced during translation.
  5. Check compliance for target market: Based on the target language-region code, identify applicable regulatory requirements from skills/context-engine/compliance-rules.md. For EU languages: GDPR consent language, cookie consent, right-to-erasure references. For hi-IN and other Indian languages: DPDPA compliance. For pt-BR: LGPD. For ko-KR: PIPA. For ja-JP: APPI. For en-US: CAN-SPAM, CCPA/CPRA where applicable. Verify that required compliance elements are present and correctly localized in the translated content — not just copied in English. Score as compliant, partially compliant (elements present but not fully localized), or non-compliant (required elements missing).
  6. Compute multilingual composite score: Calculate the weighted composite score across all four dimensions — translation technical score (40% weight, reflecting the core accuracy of the translation), content quality score (25% weight, assessing readability and structural quality in the target language), brand voice score (20% weight, measuring voice consistency across languages), and compliance score (15% weight, verifying regulatory requirements for the target market). Each dimension is scored 0-100, and the composite is the weighted average. If original content is not provided (skipping technical translation scoring), redistribute weights: content quality 40%, brand voice 35%, compliance 25%.
  7. Classify the result: Based on the composite score, assign a clear action classification — 85 and above: Publish Ready (content meets quality standards for the target market, no blocking issues, can proceed to publishing workflow), 70-84: Native Speaker Review Recommended (content is functional but has quality gaps that a native speaker should review and correct before publishing — list the specific issues requiring review), Below 70: Re-translate (content has significant quality issues that spot corrections cannot fix — recommend re-translation with specific guidance on what went wrong and which dimensions need the most improvement).
  8. Generate improvement suggestions: For any dimension scoring below its threshold (technical < 85, content quality < 80, brand voice < 75, compliance < 100), generate specific, actionable improvement suggestions. For technical issues: cite the exact problem (e.g., "Do-not-translate term 'BrandName Pro' was translated to 'BrandName Profi' on line 3 — must appear as 'BrandName Pro'"). For voice issues: cite the dimension and specific text (e.g., "Formality is at 8 in the translation but brand targets 5 — replace 'Wir freuen uns, Ihnen mitzuteilen' with 'Wir sind gespannt, euch zu zeigen'"). For compliance: cite the missing requirement and the regulation (e.g., "Missing GDPR-compliant unsubscribe link — required for all EU-targeted email content per Article 7(3)").
Show full SKILL.md (410 more words)Show less

Output

A structured multilingual quality scorecard containing:

  • Multilingual composite score: The weighted overall score (0-100) with letter grade (A+ through F) and publish/review/re-translate classification, providing an immediate actionable verdict
  • Translation technical score breakdown: Overall technical score plus the four sub-scores — length ratio (with expected range for the language pair and actual ratio), formatting preservation (with count of preserved versus damaged elements), key term consistency (with list of any violated do-not-translate terms), and placeholder integrity (with list of any missing or modified placeholders)
  • Content quality score: The eval-runner quality score for the translated content as standalone text in the target language, with dimension breakdown (structure, readability, completeness, coherence) showing how the translation performs as native content
  • Brand voice score: Voice consistency score from brand-voice-scorer.py with per-dimension breakdown (formality, energy, humor, authority, etc.) for the translated version, compared against the brand profile targets — highlighting any voice dimensions that drifted during translation
  • Compliance status: Per-regulation compliance check results for the target market — compliant, partially compliant, or non-compliant for each applicable regulation, with specific missing or incorrectly localized elements identified
  • Classification and action: Clear verdict — Publish Ready, Native Speaker Review Recommended, or Re-translate — with the reasoning based on score thresholds and any blocking issues
  • Specific issues and fix suggestions: Every issue found across all dimensions, with the exact location in the content, a description of what is wrong, and a specific suggested fix. Grouped by dimension and ordered by severity within each group
  • Baseline comparison (if available): How this score compares to the brand's multilingual quality baseline — average scores for this language pair from previous scoring runs, trend direction (improving, stable, declining), and whether this particular piece is above or below the brand's typical quality for this language

Agents Used

  • localization-specialist — Leads the multilingual scoring workflow. Executes translation technical scoring via language-router.py (length ratio, formatting, key terms, placeholders), interprets results in the context of the specific language pair's characteristics (expected expansion/contraction ratios, formatting conventions), validates do-not-translate term preservation, assesses cultural adaptation quality beyond mechanical accuracy, and synthesizes all dimension scores into the composite multilingual quality score with classification and actionable improvement suggestions
  • quality-assurance — Runs the content quality evaluation via eval-runner.py on the translated content, scoring it as standalone content in the target language. Applies the standard quality evaluation framework (structure, readability, completeness, coherence) to determine whether the translation reads well as native content, independent of translation accuracy. Checks eval configuration for brand-specific quality thresholds and ensures scoring is logged for baseline tracking via quality-tracker.py

© indranilbanerjee, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/multilingual-score of indranilbanerjee/digital-marketing-pro.

Open the folder on GitHubat commit 9e949f3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in indranilbanerjee/digital-marketing-pro, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Multilingual Score next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Personal Chinese Writing Stylesugarforever/01coder-agent-skills137—~744Automated safety check: PassMIT
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Questions about Multilingual Score

What does Multilingual Score do?

Score translated content for publication: quality, brand voice, market compliance. Multilingual Score is an agent skill from indranilbanerjee/digital-marketing-pro. Score translated content for publication: quality, brand voice, market compliance.

When should I use Multilingual Score?

Multilingual Score fits situations like: tasks that involve Translation; tasks that involve Brand voice and tone; tasks that involve Data visualization.

How do I install Multilingual Score in Claude Code?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill multilingual-score -a claude-code`. Or copy the skill folder (skills/multilingual-score in indranilbanerjee/digital-marketing-pro) into .claude/skills/multilingual-score in your project. Claude Code loads it when a task matches its description.

How do I install Multilingual Score in Codex?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill multilingual-score -a codex`. Or copy the skill folder (skills/multilingual-score in indranilbanerjee/digital-marketing-pro) into .agents/skills/multilingual-score in your project. Codex loads it when a task matches its description.

Can I use Multilingual Score in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add indranilbanerjee/digital-marketing-pro --skill multilingual-score -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/multilingual-score, .gemini/skills/multilingual-score, .github/skills/multilingual-score and .opencode/skills/multilingual-score in your project.

What does Multilingual Score need to run?

SKILL.md names no scripts, command-line tools or credentials: Multilingual Score is instructions for the agent only.

Does Multilingual Score access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Multilingual Score safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Multilingual Score use?

Multilingual Score is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Multilingual Score use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Multilingual Score?

Skills that share tags, products or a category with Multilingual Score: Translator (holaboss-ai/holaOS, 11k stars), Writers Loop (hashgraph-online/awesome-codex-plugins, 1.3k stars), Matlab Use Ncap Protocol (matlab/matlab-agentic-toolkit, 1.1k stars) and Personal Chinese Writing Style (sugarforever/01coder-agent-skills, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Multilingual Score?

indranilbanerjee (a GitHub user) maintains it in indranilbanerjee/digital-marketing-pro, which has 862 GitHub stars. The repository holds 162 skills in this directory. The repository was last updated on October 9, 2026.

Source: indranilbanerjee/digital-marketing-pro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.