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 pamelafox/presentation-skills --skill capture-video-framesgit clone --depth 1 https://github.com/pamelafox/presentation-skillsWrote 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/pamelafox/presentation-skills/capture-video-frames)<a href="https://agentmods.dev/skills/pamelafox/presentation-skills/capture-video-frames"><img src="https://agentmods.dev/badge/skills/pamelafox/presentation-skills/capture-video-frames.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 49 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00060 | $0.01627 |
| Opus 5 | $0.00030 | $0.00813 |
| Sonnet 5 | $0.00012 | $0.00325 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
capture-video-frames 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.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture and describe video frames
Step 1: Capture frames
Run the capture_video_frames.py script:
uv run .agents/skills/capture-video-frames/capture_video_frames.py <youtube_url> <output_dir> [--interval SECONDS]
Arguments
youtube_url(required): YouTube video URL (same formats accepted by the extract-transcript skill).output_dir(required): Directory to save frames and the manifest file. Created if it doesn't exist.--interval(optional): Seconds between captured frames. Defaults to 30.
Outputs
- frame_0000.png, frame_0030.png, … — PNG images named by their timestamp in seconds (zero-padded to 4 digits).
- frames_manifest.md — A markdown file listing each frame with its timestamp and a placeholder for descriptions.
Example frames_manifest.md:
| File | Timestamp | Description |
|------|-----------|-------------|
| frame_0000.png | [00:00] | |
| frame_0030.png | [00:30] | |
| frame_0060.png | [01:00] | |
Prerequisites
- yt-dlp:
brew install yt-dlporpip install yt-dlp - ffmpeg:
brew install ffmpegorapt-get install ffmpeg
Step 2: Describe frames using the describe-frame subagent
After capturing frames, describe each frame by running the describe-frame custom agent as a subagent. Each subagent invocation gets an isolated context, so frame images won't accumulate and exhaust the context window.
The describe-frame agent is defined in .github/agents/describe-frame.md.
Procedure
- Read frames_manifest.md from the output directory to get the full list of frames.
- For each frame, run the
describe-frameagent as a subagent with a prompt that includes:- The absolute path to the current frame image to view.
- The absolute path to the previous frame image to view (if one exists).
- The previous frame's description as text (if one exists).
- The subagent will return a plain-text description (or
(same as previous)if the frame is essentially identical to the previous one). - After each subagent returns, update the Description column for that row in frames_manifest.md immediately.
- Continue until all frames are described.
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.
- 8d ago First seen · 146 lines · 60 tokens per session scan A 6f546ce25ec5
capture-video-frames is a skill published in the GitHub repository pamelafox/presentation-skills (117 stars, last pushed 5d ago), licensed MIT. It adds 60 tokens to every session and 1,627 once invoked, about $0.0003 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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