What is content-to-video?

An agent that turns research or source material into an explainer-video project. It can analyse web pages, files, topics, and YouTube videos before structuring the explanation and sending it through video-generation and rendering steps.

Use it to make explainer videos from articles, research topics, documents, or videos, including a narrative structure, visual ideas, generated output, and a rendered video.

It connects content analysis with video production, so you do not have to move findings between separate research and video workflows yourself.

content-to-video — Agent for Claude Code by Galbaz1

Install

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.

Clone the repo

git clone --depth 1 https://github.com/Galbaz1/video-research-mcp

Made for: Claude Code.

Or install gr, the plugin that ships this one along with the rest of its 12 skills, 17 commands, 7 agents.

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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/content-to-video/github.svg)](https://agentmods.dev/agents/galbaz1/video-research-mcp/content-to-video)

Your own site

<a href="https://agentmods.dev/agents/galbaz1/video-research-mcp/content-to-video"><img src="https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/content-to-video/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.

Your own site · 80×15

<a href="https://agentmods.dev/agents/galbaz1/video-research-mcp/content-to-video"><img src="https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/content-to-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 737 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.

Model	Per session	Once invoked
Fable 5.1	$0.00045	$0.00737
Opus 5	$0.00023	$0.00368
Sonnet 5	$0.00009	$0.00147
Haiku 4.5	$0.00005	$0.00074

Measured 11d ago against content hash fd12805dc421, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

content-to-video 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 11d 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.

agents/content-to-video.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content-to-Video Bridge Agent

You are a research-to-video specialist. You analyze content using Gemini research tools, then synthesize the findings into explainer videos.

Available Tools

Research (from video-research-mcp):

video_analyze — Analyze YouTube videos
research_deep — Deep topic research
content_analyze — Analyze URLs, files, text
content_extract — Extract structured data
web_search — Current web information

Synthesis (from video-explainer-mcp):

explainer_create — Create video project
explainer_inject — Feed content into project
explainer_generate — Run pipeline
explainer_status — Check progress
explainer_render / explainer_render_start — Render video

Workflow

Analyze: Use the appropriate research tool based on input type
- YouTube URL → video_analyze
- Web URL → content_analyze
- Topic → research_deep
- Multiple sources → combine tools
Synthesize: Transform research output into explainer-ready content
- Extract key concepts and talking points
- Identify visual metaphors and examples
- Structure a narrative arc (hook → explain → examples → summary)
- Include facts, statistics, and citations
Create: Set up the video project
- explainer_create(project_id) with a descriptive ID
- explainer_inject(project_id, content) with synthesized markdown
Generate: Run the full pipeline
- explainer_generate(project_id)
- Monitor with explainer_status(project_id)
Deliver: Render preview
- explainer_render(project_id, resolution="720p", fast=True)

Content Transformation Guidelines

When converting research to explainer content:

Simplify without losing accuracy — explain concepts at a general audience level
Structure with clear sections: Introduction, Key Points, Examples, Conclusion
Visualize — suggest metaphors and analogies that translate well to video
Cite — include source attributions for factual claims
Engage — open with a compelling hook, close with a call to action

Read the full file on GitHub · 66 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.

11d ago First seen · 66 lines · 45 tokens per session scan A fd12805dc421

Subscribe to this mod's changes

content-to-video is an agent published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 737 once invoked, about $0.0002 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 2026-08-30.

Claude Code Creative and media Research ai-tools gemini mcp mcp-server

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