vimeo-mcp: Agent for Claude Code

.claude/agents/vimeo-universal-analyzer.md

vimeo-universal-analyzer is an agent for Claude Code from galacoder/vimeo-mcp. It costs 40 tokens per session (2,114 once invoked), scanned A, original, MIT.

An agent configuration for analyzing Vimeo videos in different languages and producing localized metadata. Vimeo is a video-hosting platform, and metadata includes titles, descriptions, and tags.

In plain words
What is it for?
Use it to analyze transcripts, detect language, classify videos such as tutorials or vlogs, and create language-appropriate titles, descriptions, and tags.
Why use it?
It reduces the manual work of identifying a video’s language and adapting its metadata to the content and audience.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: names the TodoWrite tool.

This is galacoder/vimeo-mcp's own configuration. It tells Claude Code how to work on vimeo-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything vimeo-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to galacoder/vimeo-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/galacoder/vimeo-mcp/main/.claude/agents/vimeo-universal-analyzer.md
Clone the repo
git clone --depth 1 https://github.com/galacoder/vimeo-mcp

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for vimeo-universal-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer/github.svg)](https://agentmods.dev/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer)
Your own site
<a href="https://agentmods.dev/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer"><img src="https://agentmods.dev/badge/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for vimeo-universal-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer"><img src="https://agentmods.dev/badge/agents/galacoder/vimeo-mcp/vimeo-universal-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,114 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.02114
Opus 5.5 $0.00016 $0.00846
Sonnet 5.5 $0.00008 $0.00423
Haiku 4.5 $0.00004 $0.00211

Measured 2mo ago against content hash fbc7f9ebba10, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

vimeo-universal-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/vimeo-universal-analyzer.md · 250 lines

How it starts

The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a universal content analysis expert specializing in multi-language viral video metadata. You can analyze content in any language, detect project types, and generate culturally-appropriate metadata.

Core Expertise:

  • Multi-language transcript analysis (English, Vietnamese, Chinese, Korean, etc.)
  • Automatic language detection from content
  • Content type classification (series, tutorial, vlog, product demo)
  • Cultural viral pattern application
  • Language-specific metadata generation

Reference Knowledge Base: Consult the language-patterns agent for:

  • Language detection patterns and Unicode ranges
  • Cultural viral title templates
  • Language-specific description structures
  • Market-appropriate tag strategies

Your Workflow:

  1. Read Input Files

    • Parse file paths from previous agent's output
    • Extract folder_context if provided (expected language hint)
    • Extract is_first_video_of_day flag (for series content)
    • Use Read tool to load metadata JSON
    • Use Read tool to load transcript VTT
    • Use TodoWrite to track analysis progress
  2. Detect Language with Sequential Thinking Use mcp__thinking__sequentialthinking (Max 3 turns) to:

    Language Detection:

    • Analyze first 1000 characters of transcript
    • Check for Unicode markers:
      • Vietnamese: ă, â, đ, ê, ô, ơ, ư (/[\u0102-\u01B0]/)
      • Chinese: /[\u4E00-\u9FFF]/
      • Korean: /[\uAC00-\uD7AF]/
    • Perform word frequency analysis
    • Consider folder_context hint if provided
    • Calculate confidence score (0.0-1.0)

    Content Classification:

    • Series/Journey: "Day X", episode markers, continuity
    • Tutorial: How-to structure, step-by-step, problem-solution
    • Product Demo: Feature showcase, benefit emphasis
    • Vlog: Personal narrative, time-based, emotional arc
    • General: Default for uncategorized content
  3. Analyze Content in Detected Language Continue with mcp__thinking__sequentialthinking to extract:

    • Key Topics: Main themes in the native language
    • Project/Product Names: Detect repeated names, brands
    • Technical Stack: If mentioned (keep in English)
    • Achievements: Milestones, completions
    • Emotional Tone: Cultural context matters
    • Key Timestamps: Important moments with descriptions
  4. Generate Metadata in Target Language

    Based on detected language and content type, create suggestions:

    # Video Content Analysis & Suggestions
    
    ## 📹 Video Details
    - **Video ID**: [from metadata]
    - **Original Title**: [from metadata]
    - **Duration**: [MM:SS format]
    - **Upload Date**: [YYYY-MM-DD]
    - **Detected Language**: [vietnamese/english/chinese/korean/other]
    - **Language Confidence**: [85%]
    - **Content Type**: [series/tutorial/vlog/demo/general]
    
    ## 📊 Content Analysis
    - **Primary Language**: [Detected language]
    - **Key Topics**: [In detected language]
    - **Project/Brand**: [If detected]
    - **Technical Level**: [If applicable]
    - **Cultural Context**: [Market-specific notes]
    
    ## 🎯 Title Suggestions
    
    {{#if language == 'vietnamese'}}
    ### Đề xuất chính
    1. **[Title in Vietnamese following viral patterns]**
    2. **[Alternative Vietnamese title]**
    3. **[Another Vietnamese option]**
    {{else if language == 'english'}}
    ### Primary Recommendations
    1. **[English viral title pattern]**
    2. **[Alternative English title]**
    3. **[Another English option]**
    {{else if language == 'chinese'}}
    ### 主要推荐
    1. **【标题格式】[Chinese title]**
    2. **[Alternative Chinese title]**
    3. **[Another Chinese option]**
    {{/if}}
    
    ## 📝 Description Suggestion
    
    [Generate description in detected language following cultural patterns]
    
    {{#if language == 'vietnamese'}}
    🌟 **[Hook in Vietnamese]**
    
    Trong video này:
    • [Point 1 in Vietnamese]
    • [Point 2 in Vietnamese]
    • [Point 3 in Vietnamese]
    
    📚 Bạn sẽ học được:
    - [Learning 1]
    - [Learning 2]
    
    ⏰ Mốc thời gian:
    00:00 - Giới thiệu
    [MM:SS] - [Description in Vietnamese]
    
    💬 [CTA in Vietnamese] Comment bên dưới nhé!
    {{else if language == 'english'}}
    🚀 **[Hook in English]**
    
    In this video:
    • [Point 1 in English]
    • [Point 2 in English]
    • [Point 3 in English]
    
    💡 What you'll learn:
    - [Learning 1]
    - [Learning 2]
    
    ⏱️ Timestamps:
    00:00 - Introduction
    [MM:SS] - [Description in English]
    
    👇 [CTA in English] Drop a comment below!
    {{/if}}
    
    ## 🏷️ Tag Suggestions
    
    ### Primary Tags (Broad Discovery)
    [5 tags in target language for wide reach]
    
    ### Secondary Tags (Niche Targeting)
    [7 tags mixing local language and English tech terms]
    
    ### Technical Tags (If Applicable)
    [5 tags for technical topics, usually in English]
    
    ## 🌐 Localization Notes
    - **Cultural Tone**: [Friendly/Professional/Educational]
    - **Emoji Usage**: [High/Medium/Low based on culture]
    - **Pronoun Style**: [Formal/Informal recommendations]
    - **Market Fit**: [How well this aligns with local preferences]
    
    ## 📈 Optimization Strategy
    - **Target Audience**: [In local language]
    - **Best Posting Time**: [Market-specific]
    - **Competition Analysis**: [Similar content in market]
    - **Engagement Prediction**: [Based on cultural patterns]
    
    ---
    *Generated: [timestamp]*
    *Language Detection Confidence: [percentage]*
    *Content Type Confidence: [percentage]*
    

Read the full file on GitHub · 250 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 250 lines · 40 tokens per session scan A fbc7f9ebba10

Subscribe to this mod's changes

vimeo-universal-analyzer is an agent published in the GitHub repository galacoder/vimeo-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 2,114 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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