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-social-media-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/samuraigpt/generative-media-skills/muapi-social-media-video)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/muapi-social-media-video"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-social-media-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/samuraigpt/generative-media-skills/muapi-social-media-video"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-social-media-video.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 | $0.00060 | $0.02912 |
| Opus 5 | $0.00030 | $0.01456 |
| Sonnet 5 | $0.00012 | $0.00582 |
| Haiku 4.5 | $0.00006 | $0.00291 |
Grade A, and why
muapi-social-media-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 13d 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-social-media-video — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Video Creator
End-to-end pipeline: Brand Files → Storyboard → Reference Images → Seedance 2.0 Video.
Reads your brand identity, ICP, and messaging documents to produce on-brand social video — fully optimized for Seedance 2.0's instructional prompt grammar and your target platform.
Agent Execution Protocol
Step 1 — Read Brand Files
Before writing anything, the agent MUST read all available brand files. Look for them in the working directory or any brand/ subdirectory:
| File | What to extract |
|---|---|
brand-identity.md |
Visual style, color palette, tone, logo/product aesthetics, brand personality |
ICP.md |
Target audience — who they are, their pain points, what motivates them |
messaging.md |
Core value props, hooks, CTAs, campaign themes, taglines |
If a file is missing, proceed with what's available and note the gap.
Step 2 — Write the Social Post + Storyboard
Use brand context to produce:
Social Post Copy (for caption/copy):
- Hook line (first 1–2 sentences — must stop the scroll)
- Body (3–5 sentences: problem → solution → proof → CTA)
- Hashtags (5–8 relevant tags)
- CTA (one clear action)
Storyboard (match duration to platform spec):
0–3s: [Opening scene — hook/visual surprise — camera move]
3–7s: [Core message — product/subject in action — camera move]
7–10s: [Resolution — brand moment — CTA text on screen]
10–15s: [Logo/tagline hold — background music fade]
Tone must match brand personality. If brand is playful → upbeat transitions, bright grade. If brand is premium → slow reveals, dark luxury aesthetic, moody lighting.
Step 3 — Craft the Seedance 2.0 Director Prompt
Transform the storyboard into a technical Director Brief for Seedance 2.0.
Rules:
- Never use vague descriptors ("beautiful", "amazing", "8k"). Use technical cinematography language.
- Always specify camera movement, lens type, and lighting physically.
- For 10s+ videos, use timecode segments:
0–3s: [...] 3–7s: [...] 7–10s: [...] - Integrate
@image1,@image2reference tags if images are provided. - Always include sound direction (even brief) — Seedance generates audio.
- Lead with composition, end with texture and micro-motion.
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.
- 13d ago First seen · 278 lines · 60 tokens per session scan A 8990bd91d06e
muapi-social-media-video is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,263 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 2,912 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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mlops-validation
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mlops-prototyping
Structure reproducible Jupyter notebooks with a fixed section layout, hoisted configuration, and leakage-free scikit-learn pipelines. Use when exploring a dataset, training a first model, or preparing a notebook for promotion.
mlops-collaboration
Prepare a project for public collaboration — license, code of conduct, docs, branch rulesets, templates, and git-cliff releases. Use when open-sourcing a repository, onboarding contributors, or cutting a tagged release.
mlops-observability
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mlops-industrialization
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