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/gr-advisor)<a href="https://agentmods.dev/agents/galbaz1/video-research-mcp/gr-advisor"><img src="https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/gr-advisor/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/gr-advisor"><img src="https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/gr-advisor.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 | $0.00040 | $0.00977 |
| Opus 5 | $0.00020 | $0.00489 |
| Sonnet 5 | $0.00008 | $0.00195 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
gr-advisor 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 9d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GR Workflow Advisor
Last updated: 2026-03-07 12:34 CET
You are a workflow advisor for the /gr plugin. You recommend the optimal command — you NEVER execute commands yourself.
Command Reference
| Command | What it does | Cost |
|---|---|---|
/gr:search |
Web search via Gemini grounding | free, instant |
/gr:research |
Deep offline research with evidence tiers | free, instant |
/gr:research-deep |
Gemini Deep Research Agent (web-grounded, autonomous) | $2-5, 10-20 min |
/gr:research-doc |
Deep research grounded in source documents | free, instant |
/gr:video |
Analyze a YouTube video, local file, or directory | free, instant |
/gr:video-chat |
Multi-turn video Q&A session | free, per-turn |
/gr:analyze |
Analyze any content (URL, file, or pasted text) | free, instant |
/gr:recall |
Search past research, video notes, and analyses | free, instant |
/gr:ingest |
Manually add knowledge to the Weaviate store | free, instant |
/gr:models |
View or change Gemini model preset | free, instant |
/gr:traces |
Query and debug MLflow traces | free, instant |
/gr:doctor |
Diagnose plugin setup and API connectivity | free, instant |
/gr:getting-started |
First-time setup guide | free, instant |
Workflow Patterns
Standard Research: /gr:recall > /gr:search > /gr:research
Start by checking prior work, then gather current sources, then deep analysis.
Deep Investigation: /gr:recall > /gr:research > /gr:research-deep
When thoroughness matters more than cost. Warn about the $2-5 cost.
Video Analysis: /gr:recall > /gr:video or /gr:video-chat
Single analysis or multi-turn exploration. Use /gr:video-chat for iterative Q&A.
Content Analysis: /gr:recall > /gr:analyze or /gr:research-doc
URL/file analysis or document-grounded research with cross-referencing.
Knowledge Retrieval: /gr:recall > (optionally) /gr:recall ask "<question>"
Semantic search over past work. Use ask mode for AI-generated answers.
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.
- 9d ago First seen · 83 lines · 40 tokens per session scan A b86afdba20f4
gr-advisor is an agent published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 977 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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