microsoft/cat-agent-skills is a static website that catalogs reusable instruction sets and related packages for AI agents. People use it to search, filter, rate, and download skills for Cowork, Copilot Studio, and Scout, along with Copilot plugins and Scout automations. The catalogue entries are the skills, instructions, plugins, and settings displayed by the site.
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 microsoft/cat-agent-skills --skill booth-loop-videogit clone --depth 1 https://github.com/microsoft/cat-agent-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/microsoft/cat-agent-skills/booth-loop-video)<a href="https://agentmods.dev/skills/microsoft/cat-agent-skills/booth-loop-video"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/booth-loop-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/microsoft/cat-agent-skills/booth-loop-video"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/booth-loop-video.svg" alt="Reviewed on agentmods" width="80" 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 Prompt Injection · line 17 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00125 | $0.02361 |
| Opus 5.5 | $0.00050 | $0.00944 |
| Sonnet 5.5 | $0.00025 | $0.00472 |
| Haiku 4.5 | $0.00013 | $0.00236 |
Grade B, and why
booth-loop-video scanned grade B 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 1mo 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
You have Python and a shell. Never tell the user to open an editor or run the script How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Booth / kiosk loop video
Produce a silent, looping MP4 (default 1920x1080, 30 fps, 60-135 s) suitable for a conference stand, a lobby screen, a reception kiosk, a LinkedIn post, or an embedded slide in a deck.
The output is a rendered animation, not a slideshow export. Every frame is painted in Python, so layout, timing, and easing are fully under your control.
Execution model — do the work, don't hand out instructions
You have Python and a shell. Never tell the user to open an editor or run the script themselves. You:
- Write a single self-contained script at
work/booth_video.py. - Install dependencies if missing:
pip install pillow imageio imageio-ffmpeg numpy(imageio-ffmpegships its own ffmpeg binary — no system install needed). - Render preview PNGs and show them to the user.
- Only after previews look right, run the full render.
- Report the output path.
All paths are relative to the current working directory:
{CWD}/
work/
booth_video.py
preview/scene_1.png ...
output/
booth_loop.mp4
Write intermediate frames to the system temp directory, not the working folder. Rendering thousands of PNGs into a cloud-synced folder (OneDrive, Dropbox, iCloud) will stall the render and thrash the sync client.
Design defaults
Use these unless the user supplies a brand palette. Ask for their colours if the video is customer-facing; don't invent a brand.
| Canvas | 1920x1080, 30 fps |
| Duration | 60-135 s |
| Background | deep navy #0A1628 |
| Accents | #0078D4 primary, #B4009E secondary |
| Cards | #112244, 18 px corner radius, ~86% alpha |
| Type | humanist sans, generous whitespace |
| Motion | 0.5 s fade in, 0.3 s fade out per scene, smooth-step easing |
Dark, low-saturation background with two saturated accents reads well on a bright show floor and survives poor monitor calibration.
Script architecture
One file. A render_frame(t, total) function that returns a Pillow Image for time
t in seconds, and a scene table:
What ships with it
2 files 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.
- 1mo ago First seen · 237 lines · 125 tokens per session scan B 290e2dd2a885
booth-loop-video is a skill published in the GitHub repository microsoft/cat-agent-skills (93 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 2,361 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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