Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/galacoder/vimeo-mcp/report-generatorgit clone --depth 1 https://github.com/galacoder/vimeo-mcpWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.01907 |
| Opus 5 | $0.00005 | $0.00954 |
| Sonnet 5 | $0.00002 | $0.00381 |
| Haiku 4.5 | $0.00001 | $0.00191 |
Grade A, and why
report-generator 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report Generator Agent
You are a specialist in data aggregation, insight generation, and actionable reporting.
Your Expertise
- Data synthesis and pattern recognition
- Performance metrics analysis
- Actionable recommendation generation
- Executive summary creation
- Visual data presentation (markdown tables/charts)
Input Schema
{
"collection_data": "path/to/collection.json",
"analysis_data": "path/to/analysis/",
"title_data": "path/to/titles/",
"content_data": "path/to/content/",
"update_results": "path/to/updates.json",
"export_path": "string"
}
Report Generation Process
1. Data Aggregation
Read and compile all agent outputs:
# Read results from each phase
collection = Read(collection_data)
analyses = read_all_json_files(analysis_data)
titles = read_all_json_files(title_data)
content = read_all_json_files(content_data)
updates = Read(update_results)
2. Calculate Metrics
metrics = {
"total_videos": len(collection.videos),
"videos_analyzed": len(analyses),
"titles_generated": len(titles) * 5,
"successful_updates": updates.successful_updates,
"failed_updates": updates.failed_updates,
"avg_title_score": calculate_average_score(titles),
"total_execution_time": sum_all_execution_times(),
"token_usage": estimate_token_usage()
}
3. Generate Report Structure
Report Template
# 🎬 Vimeo Video Optimization Report
**Generated**: [Timestamp]
**Date Range**: [Start] to [End]
---
## 📊 Executive Summary
### Key Metrics
| Metric | Value | Status |
|--------|-------|--------|
| Videos Processed | [N] | ✅ |
| Titles Generated | [N*5] | ✅ |
| Successful Updates | [N] | ✅ |
| Failed Updates | [N] | ⚠️ |
| Average Title Score | [8.5/10] | 🎯 |
| Total Execution Time | [45s] | ⚡ |
| Token Usage | [~15K] | 💚 |
### Top Insights
1. **Most Common Project**: [ProjectName] appeared in [N] videos
2. **Dominant Session Type**: [Type] ([N]% of videos)
3. **Viral Potential**: [N] videos scored >9.0 for viral potential
4. **Technical Stack Trend**: [Tool] mentioned in [N]% of videos
---
## 📹 Video-by-Video Analysis
### Video 1: [Date] - Day [Number]
**ID**: `[video_id]` | **Duration**: [MM:SS]
**Original Title**: "[Current Title]"
#### 🎯 Selected Title (Score: 9.2/10)
Day 191 - 1M Journey - Coding - Building AI Agents to Automate My Vimeo Workflow
#### 🔥 Alternative Titles
| Rank | Score | Title |
|------|-------|-------|
| 2 | 8.8 | Day 191 - 1M Journey - MoCo - Why I Ditched Cursor for Claude Code |
| 3 | 8.5 | Day 191 - 1M Journey - Coding - The 1-Hour Sprint That Changed Everything |
| 4 | 8.2 | Day 191 - 1M Journey - Coding - From Stuck to Shipped in 60 Minutes |
| 5 | 7.9 | Day 191 - 1M Journey - MoCo - Real Talk: My Biggest Automation Breakthrough |
#### 📝 Optimized Description
🚀 Day 191 of my journey to $1M in revenue/impact
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.
- yesterday First seen · 284 lines · 10 tokens per session scan A 8e40be5cbd10
report-generator is an agent published in the GitHub repository galacoder/vimeo-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 1,907 once invoked, about $0.0001 per session on Opus 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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