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.
git clone --depth 1 https://github.com/Galbaz1/video-research-mcpWrote 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.
[](https://agentmods.dev/agents/galbaz1/video-research-mcp/content-to-video)<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.
<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>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 | $0.01105 |
| Opus 5.5 | $0.00018 | $0.00442 |
| Sonnet 5.5 | $0.00009 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 72 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 videosresearch_execute— Explicit model-only, supplied, protected URL or hybrid research with joined budgets and retained packetsresearch_web/research_web_status— Hosted grounded research with durable job status; internal search/token/USD ceilings remain unverifiedresearch_deep— Three-phase model-only synthesis; no observed retrievalcontent_analyze— Analyze URLs, files, textcontent_extract— Extract structured dataweb_search— Current web information
Synthesis (from video-explainer-mcp):
explainer_create— Create video projectexplainer_inject— Feed content into projectexplainer_generate— Run pipelineexplainer_status— Check progressexplainer_render/explainer_render_start— Render video
Workflow
-
Analyze: Use the appropriate research tool based on input type
- YouTube URL →
video_analyze - Web URL → prepare
research_execute(mode="retrieval")with explicit permitted URLs/domains - Existing original evidence →
research_execute(mode="supplied" or "hybrid")with its exact packet/root - Topic → explicitly label
research_execute(mode="model_only")as an unverified model draft, or use authorized hostedresearch_webfor fresh source leads - Dry preparation makes no provider/URL calls. Execution requires the actual relevant authority; configured credentials and a green plan supply no spend authority.
- Preserve every rejected source, failed branch, revision, contradiction and abstention. Stop at its declared allowance. Save the executed run ID; exact replay verifies retained bytes and never automatically repeats ambiguous submissions.
- YouTube URL →
-
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)
- Retain source/page/time/claim IDs from the returned packet. Exact quote presence is separate from semantic or factual acceptance; every model tier stays a proposal.
- Preserve supplied editorial approvals and lineage; new findings remain unapproved. Carry unsupported additions and contradictory evidence into the draft report and flag them before script/storyboard use.
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.
- 25d ago Changed · +7 lines 8e108f524c33
- 27d ago Changed · -1 lines e0d7cabc0416
- 1mo ago First seen · 66 lines · 45 tokens per session scan A fd12805dc421
content-to-video is an agent published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 4d ago), licensed MIT. It adds 45 tokens to every session and 1,105 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 2026-08-30.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.