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-seedance-2Wrote 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-seedance-2)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/muapi-seedance-2"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-seedance-2/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-seedance-2"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-seedance-2.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 | $0.00067 | $0.07060 |
| Opus 5.5 | $0.00027 | $0.02824 |
| Sonnet 5.5 | $0.00013 | $0.01412 |
| Haiku 4.5 | $0.00007 | $0.00706 |
Grade A, and why
muapi-seedance-2 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-seedance-2 — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 633 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🎬 Seedance 2.0 Cinema Expert
The definitive skill for "Director-Level" AI video orchestration. Seedance 2.0 is not a descriptive model; it is an instructional model. It responds best to technical cinematography, physics directives, and precise camera grammar.
Core Competencies
- Text-to-Video (t2v): Generate cinematic video from a Director Brief — Chinese, Global, or VIP tier.
- Image-to-Video (i2v): Animate 1–9 reference images — Chinese, Global (smart mode), or VIP tier.
- Video Extension (extend): Seamlessly continue an existing Seedance 2.0 video (Chinese tier).
- First & Last Frame (first-last): Interpolate a fluid video between a start image and end image (Global/VIP).
- Omni Reference (omni): Full multimodal reference with images + audio + character refs (all tiers).
- Omni Reference Training (omni-train): Train a custom persistent character for identity-consistent generation.
- Character Sheet (character): Build a reusable character from 1–3 images (Chinese tier).
- Video Edit (video-edit): Edit an existing video with a prompt + optional reference images (Chinese tier).
- Watermark Removal (watermark-remove): Strip Seedance 2.0 watermarks (basic or Pro).
🏷️ Tiers
| Tier | Flag | Censorship | Aspect Ratios | Duration | Quality param |
|---|---|---|---|---|---|
| Chinese (default) | --tier chinese |
Low | 16:9, 9:16, 4:3, 3:4 | 5 / 10 / 15 s | Yes (basic/high) |
| Global | --tier global |
Standard | + 21:9, 1:1 | Any 4–15 s | No |
| VIP | --tier vip |
Low | + 21:9, 1:1 | Any 4–15 s | No |
Add --fast to any Global or VIP call to use the fast-queue variant (lower latency, same quality).
📥 Input Limits
| Input Type | Chinese i2v/omni | Global/VIP i2v/omni | Formats | Max Size |
|---|---|---|---|---|
| Images | ≤ 9 | ≤ 9 | jpeg, png, webp | 30 MB each |
| Videos | ≤ 3 (omni only) | Not supported | mp4, mov | 50 MB each |
| Audio | ≤ 3 | ≤ 3 | mp3, wav | 15 MB each |
| First-Last | — | 1–2 images | jpeg, png, webp | 30 MB each |
| Video Edit | 1 video + ≤ 9 imgs | — | mp4 ≤ 10 MB / 15s | — |
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 · 633 lines · 67 tokens per session scan A 21e344f0ac69
muapi-seedance-2 is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (25 stars, last pushed 28d ago), licensed MIT. It adds 67 tokens to every session and 7,060 once invoked, about $0.0003 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.
Other skills, from other repositories
mlops-automation
Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.
mlops-validation
Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.
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-observability
Make an ML system a glass box with reproducible runs, MLflow dataset lineage, drift monitoring, alerting, and SHAP explanations. Use when a deployed model needs traceability, monitoring, alerting, or explanation.
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-industrialization
Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.