video-research

video-research is a skill for Claude Code from Galbaz1/video-research-mcp. It costs 49 tokens per session (3,502 once invoked), scanned A, original, MIT.

A guide for using 28 tools that analyze videos, research topics, extract content, search the web, and store knowledge. It works with video-research MCP, a server that provides these tools, including YouTube-related tools.

In plain words
What is it for?
Getting YouTube metadata, analyzing videos, discussing a video across multiple turns, researching topics, checking claims, and running web-based research.
Why use it?
It helps an agent choose the right tool and write precise instructions for each research or video task. This reduces guesswork when requesting structured results.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the gr plugin — 12 skills, 17 commands, 7 agents shipped together

Good fit Getting YouTube metadata, analyzing videos, discussing a video across multiple turns, researching topics, checking claims, and running web-based research.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/galbaz1/video-research-mcp/video-research
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.

Any agent
npx skills add Galbaz1/video-research-mcp --skill video-research
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.

agentmods badge for video-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/video-research/github.svg)](https://agentmods.dev/skills/galbaz1/video-research-mcp/video-research)
Your own site
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/video-research"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/video-research/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 video-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/video-research"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/video-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,502 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 $0.00049 $0.03502
Opus 5 $0.00024 $0.01751
Sonnet 5 $0.00010 $0.00700
Haiku 4.5 $0.00005 $0.00350

Measured 12d ago against content hash 0862955b6612, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

video-research 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 12d 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.

skills/video-research/SKILL.md · 354 lines

How it starts

The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video Research MCP — Tool Usage Guide

You have access to the video-research-mcp MCP server, which exposes 28 tools powered by Gemini 3.1 Pro and the YouTube Data API. These tools are instruction-driven — you write the instruction, Gemini returns structured JSON. Three tools (video_metadata, video_comments, video_playlist) use the YouTube Data API directly for fast metadata retrieval without Gemini inference.

Core Principle

Tools accept an instruction parameter instead of fixed modes. Write specific, actionable instructions. The more precise your instruction, the better the structured output.

Tool Selection Guide

I want to... Use this tool
Get video title, stats, duration, tags video_metadata
List videos in a YouTube playlist video_playlist
Analyze a YouTube video video_analyze
Have a multi-turn conversation about a video video_create_session + video_continue_session
Research a topic in depth research_deep
Plan a research strategy research_plan
Verify a specific claim research_assess_evidence
Launch long-running web-grounded deep research research_web
Poll status of a running deep-research job research_web_status
Ask follow-up questions on a completed deep-research report research_web_followup
Cancel a running deep-research job research_web_cancel
Analyze a URL, file, or text content_analyze
Batch-analyze a folder of documents content_batch_analyze
Extract structured data from content content_extract
Deep research grounded in documents research_document
Search the web for current info web_search
Check or clear the cache infra_cache
Change model/thinking/temperature infra_configure
Find past analyses and research /gr:recall "topic" (semantic search when Weaviate configured)
Get AI answer from past work /gr:recall ask "question" (requires Weaviate + weaviate-agents)
Browse knowledge gaps /gr:recall fuzzy or /gr:recall unknown

Read the full file on GitHub · 354 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. 12d ago First seen · 354 lines · 49 tokens per session scan A 0862955b6612

Subscribe to this mod's changes

video-research is a skill published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,502 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.

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