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 shaharsha/claude-skills --skill viewing-videosgit clone --depth 1 https://github.com/shaharsha/claude-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/shaharsha/claude-skills/viewing-videos)<a href="https://agentmods.dev/skills/shaharsha/claude-skills/viewing-videos"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/viewing-videos/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/shaharsha/claude-skills/viewing-videos"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/viewing-videos.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 | $0.00131 | $0.01244 |
| Opus 5.5 | $0.00052 | $0.00498 |
| Sonnet 5.5 | $0.00026 | $0.00249 |
| Haiku 4.5 | $0.00013 | $0.00124 |
Grade C, and why
viewing-videos scanned grade C with 1 finding 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 2mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf /tmp/frames && mkdir -p /tmp/frames How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Viewing Videos
Overview
Claude can't watch video, but it reads images well. Turn the video into the fewest frames that answer the question with ffmpeg, triage them on a contact sheet, then Read the interesting ones at full resolution. Requires ffmpeg + ImageMagick montage (both via Homebrew).
Step 0: Probe first
ffprobe -v error -show_entries format=duration:stream=codec_type,width,height -of default=noprint_wrappers=1 video.mp4
Duration picks the strategy; resolution sets expectations — 1080p+ screen content reads fine, at 720p big KPI numbers are fine but dense tables/spreadsheets need the crop+upscale step below.
Pick a strategy
| Situation | Strategy |
|---|---|
| You know the moment (timestamped transcript, user says "at 12:34") | Targeted single frames |
| Screen shares / slides / UI demos, unknown moments | Scene-change sweep (default) |
| Continuous motion (camera footage, gameplay) — scenes never "change" | Interval sampling (see frame budget below) |
Targeted frame
-ss before -i = fast seek:
ffmpeg -ss 12:34 -i video.mp4 -frames:v 1 /tmp/frames/t12m34s.png
Scene-change sweep → timestamp names → contact sheet
rm -rf /tmp/frames && mkdir -p /tmp/frames
ffmpeg -hide_banner -i video.mp4 -vf "select='gt(scene,0.2)',showinfo" -fps_mode vfr /tmp/frames/frame_%03d.png 2> /tmp/frames/log.txt
cd /tmp/frames && grep -o 'pts_time:[0-9.]*' log.txt | cut -d: -f2 | awk '{n++; t=int($1); printf "mv frame_%03d.png t%02dm%02ds.png\n", n, t/60, t%60}' | sh
montage -label '%t' -font /System/Library/Fonts/Helvetica.ttc t*.png -tile 6x -geometry 320x180+4+4 -pointsize 18 contact_%d.png
Read contact_0.png to triage (timestamps are printed under each tile), then Read only the frames that matter at full resolution. Threshold 0.2 works for meetings: webcam segments cluster harmlessly, screen shares fire exactly on slide/page changes. Footage with constant motion → hundreds of frames; switch to interval sampling.
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.
- 2mo ago First seen · 76 lines · 131 tokens per session scan C 46bbb8560e71
viewing-videos is a skill published in the GitHub repository shaharsha/claude-skills (6 stars, last pushed today), licensed MIT. It adds 131 tokens to every session and 1,244 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
viewing-videos compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| viewing-videosshaharsha/claude-skills | Skill | 6 | today | MIT | C | 131 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |