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 skills add xuansenpa1/skillrevise --skill gemini-video-understandinggit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/skills/xuansenpa1/skillrevise/gemini-video-understanding)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/gemini-video-understanding"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/gemini-video-understanding/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/skills/xuansenpa1/skillrevise/gemini-video-understanding"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/gemini-video-understanding.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 | $0.00047 | $0.02307 |
| Opus 5 | $0.00023 | $0.01154 |
| Sonnet 5 | $0.00009 | $0.00461 |
| Haiku 4.5 | $0.00005 | $0.00231 |
Grade A, and why
gemini-video-understanding 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 8d 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.
This is a copy
100% identical to gemini-video-understanding — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Video Understanding Skill
Purpose
This skill enables video understanding workflows using the Google Gemini API, including video summarization, question answering, transcription with optional visual descriptions, timestamp-based queries (MM:SS), scene/timeline detection, video clipping, custom FPS sampling, multi-video comparison, and YouTube URL analysis.
When to Use
- Summarizing a video into key points or chapters
- Answering questions about what happens at specific timestamps (MM:SS)
- Producing a transcript (optionally with visual context) and speaker labels
- Detecting scene changes or building a timeline of events
- Analyzing long videos by clipping to relevant segments or reducing FPS
- Comparing multiple videos (up to 10 videos on Gemini 2.5+)
- Analyzing public YouTube videos directly via URL
Required Libraries
The following Python libraries are required:
from google import genai
from google.genai import types
import os
import time
Input Requirements
- File formats: MP4, MPEG, MOV, AVI, FLV, MPG, WebM, WMV, 3GPP
- Size constraints:
- Use inline bytes for small files (rule of thumb: <20MB).
- Use the File API upload flow for larger videos (most real videos).
- YouTube:
- Video must be public (not private/unlisted) and not age-restricted.
- Provide a valid YouTube URL.
- Duration / context window (model-dependent):
- 2M-token models: ~2 hours (default resolution) or ~6 hours (low-res).
- 1M-token models: ~1 hour (default) or ~3 hours (low-res).
- Timestamps: Use MM:SS (e.g.,
01:15) when requesting time-based answers.
Output Schema
All extracted/derived content should be returned as valid JSON conforming to this schema:
{
"success": true,
"source": {
"type": "file|youtube",
"id": "video.mp4|VIDEO_ID_OR_URL",
"model": "gemini-2.5-flash"
},
"summary": "Concise summary of the video...",
"transcript": {
"available": true,
"text": "Full transcript text (may include speaker labels)...",
"includes_visual_descriptions": true
},
"events": [
{
"timestamp": "MM:SS",
"description": "What happens at this time",
"category": "scene_change|key_point|action|other"
}
],
"warnings": [
"Optional warnings about limitations, missing timestamps, or low confidence areas"
]
}
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.
- 8d ago First seen · 315 lines · 47 tokens per session scan A 49f3ca8f3618
gemini-video-understanding is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 7d ago), licensed MIT. It adds 47 tokens to every session and 2,307 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gemini-video-understanding, differing in 0 lines, and is treated as a copy.
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