Generative-Media-Skills is a toolkit that lets AI agents generate, edit, and display images, videos, and audio through the muapi command-line interface. It is for users of Claude Code, Cursor, Gemini CLI, and OpenCode who need multimodal media-generation workflows. The catalogue entries are the skills that expose these media capabilities to coding agents.
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/SamurAIGPT/Generative-Media-Skillsnpx agentmods add skills/samuraigpt/generative-media-skills/muapi-ai-clippingWrote 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/samuraigpt/generative-media-skills/muapi-ai-clipping)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/muapi-ai-clipping"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-ai-clipping/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/samuraigpt/generative-media-skills/muapi-ai-clipping"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-ai-clipping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00096 | $0.01844 |
| Opus 5.5 | $0.00038 | $0.00738 |
| Sonnet 5.5 | $0.00019 | $0.00369 |
| Haiku 4.5 | $0.00010 | $0.00184 |
Grade A, and why
muapi-ai-clipping 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- muapi-ai-clipping — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Clipping
One API call: long video in → ranked vertical short clips out.
Each clip ships with a viral score (0–100), an opening hook line, a one-sentence "why it works" reason, and a hosted mp4 URL.
Underlying API: https://muapi.ai/playground/ai-clipping Reference implementation (open source): https://github.com/SamurAIGPT/AI-Youtube-Shorts-Generator
When to Use
- Auto-clip a podcast, interview, lecture, vlog, or stream into TikTok / Reels / Shorts.
- Extract the best 30–75s moments from any hosted video URL.
- Get face-tracked vertical (9:16), square (1:1), or portrait (4:5) crops without running ffmpeg locally.
If you only need raw timestamps for your own renderer, set --coords-only to skip cropping and just get the highlight ranges.
Agent Execution Protocol
Step 1 — Collect Inputs
| Input | Required | Default | Notes |
|---|---|---|---|
--video |
yes | — | Hosted mp4 URL, or local file path (auto-uploaded), or YouTube URL (if backend supports it) |
--num-clips |
no | 3 |
Number of highlights to extract |
--aspect-ratio |
no | 9:16 |
9:16 | 1:1 | 4:5 |
--coords-only |
no | off | Return just the highlight time ranges, skip cropping |
If the user gave only a video URL, run with defaults — don't block on questions.
Step 2 — Verify Prerequisites
muapi-cliinstalled and authed (muapi auth configure)MUAPI_API_KEYavailable (env var ormuapi auth statuspasses)
That's it. No ffmpeg, no Python, no Whisper install, no LLM keys. Everything runs server-side.
Step 3 — Run the Skill
bash library/edit/ai-clipping/scripts/run-ai-clipping.sh \
--video "https://example.com/podcast.mp4" \
--num-clips 5 \
--aspect-ratio 9:16 \
--view
The script:
- Resolves
--videoto a hosted URL (uploads local files viamuapi upload fileif needed). - Calls
muapi edit clippingwith the supported parameters. - Polls until the job is done (or returns the
request_idimmediately under--async). - Prints a ranked summary and, if
--output-jsonis set, writes the full result.
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 · 195 lines · 96 tokens per session scan A df222c2fa2c8
muapi-ai-clipping is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (25 stars, last pushed 28d ago), licensed MIT. It adds 96 tokens to every session and 1,844 once invoked, about $0.0004 per session on Opus 5.5. 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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