Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/krusemediallc/cursor-ad-agentnpx agentmods add skills/krusemediallc/cursor-ad-agent/analyze-videoWrote 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/krusemediallc/cursor-ad-agent/analyze-video)<a href="https://agentmods.dev/skills/krusemediallc/cursor-ad-agent/analyze-video"><img src="https://agentmods.dev/badge/skills/krusemediallc/cursor-ad-agent/analyze-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/skills/krusemediallc/cursor-ad-agent/analyze-video"><img src="https://agentmods.dev/badge/skills/krusemediallc/cursor-ad-agent/analyze-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 | $0.00134 | $0.04207 |
| Opus 5 | $0.00067 | $0.02103 |
| Sonnet 5 | $0.00027 | $0.00841 |
| Haiku 4.5 | $0.00013 | $0.00421 |
Grade A, and why
analyze-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 10d 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
95% identical to analyze-video — 15 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 — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Video → Reusable Prompting Template
Someone found a video style they love. Your job is to deconstruct it into a reusable prompting template — a formula they can plug any product, person, or setting into and get that same style back from Seedance 2.0.
Critical constraint: Seedance 2.0 has a 15-second maximum per clip. The reference video may be longer than 15 seconds (often 30-60s). Your template must be designed for 15-second output — which means distilling the style's essence into what can be captured in a single 15-second clip, and providing a multi-clip strategy for recreating the full effect of longer-form styles across a series of clips.
The output is NOT a single prompt. It's a template skill saved to
skills/arcads-external-api/prompting/prompt-library/ that works the same way
seedance-2-ugc.md works — a documented formula
with layers, variables, options, and examples that the agent can use to generate unlimited
prompts in that style.
Dependencies
- ffmpeg / ffprobe — required for frame extraction (Step 1). Install via
brew install ffmpegon macOS. - Whisper transcription — use
npx hyperframes transcribe(preferred; it manages whisper.cpp and model downloads) or the optionalopenai-whisperPython package. If neither is available, ask the user to provide dialogue manually.
Inputs
- Video file (required): path to
.mp4,.mov,.webm, or similar - Style name (optional): what to call this template (e.g., "car-review", "unboxing-hype", "skeptic-converted"). If not provided, you'll name it based on what you observe.
Step 1: Extract frames and audio
Run the extraction script:
bash "skills/arcads-external-api/prompting/analyze-video/scripts/extract-frames.sh" "<video_path>" "/tmp/video-analysis" <num_frames>
Frame count by duration:
- Under 10s → 8 frames
- 10-20s → 12 frames
- 20-30s → 16 frames
- Over 30s → 20 frames
Read metadata.txt for duration, resolution, and fps.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 385 lines · 134 tokens per session scan A 7952b8f69270
analyze-video is a skill published in the GitHub repository krusemediallc/cursor-ad-agent (10 stars, last pushed 1mo ago), licensed MIT. It adds 134 tokens to every session and 4,207 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to analyze-video, differing in 15 lines, and is treated as a copy.
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