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
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 SamurAIGPT/Generative-Media-Skills --skill cartoon-dance-animationgit clone --depth 1 https://github.com/SamurAIGPT/Generative-Media-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/samuraigpt/generative-media-skills/cartoon-dance-animation)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/cartoon-dance-animation"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/cartoon-dance-animation/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/cartoon-dance-animation"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/cartoon-dance-animation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Data Exfiltration · line 61 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 61 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00035 | $0.01087 |
| Opus 5 | $0.00017 | $0.00544 |
| Sonnet 5 | $0.00007 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade B, and why
muapi-cartoon-dance-animation scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- For model IDs without a CLI alias yet, fall back to the raw endpoint via `curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}'` and poll with Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- For model IDs without a CLI alias yet, fall back to the raw endpoint via `curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}'` and poll with How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cartoon Dance Animation
Convert a photo of a person into a Pixar-style 3D cartoon character, then animate it using a reference dance or motion video.
Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
user_image |
image_url | yes | — | A clear full-body or medium-shot photo of the person to be cartoonified. |
reference_video |
video_url | no | — | A video containing the specific dance or motion to apply to the character. |
Steps
Phase A — Cartoon Character Generation
If {{user_image}} is not provided, ask the user to upload their photo.
Once the photo is available, submit the plan with ONE step to cartoonify the image:
- Image Generation —
muapi image edit(model=nano-banana-2-edit):- Reference Image:
{{user_image}} - Prompt:
Use the uploaded input photo as the exact same person in the final render. Preserve identity accurately: same face shape, eyes, nose, lips, jawline, skin tone, hairstyle, hairline, expression, age, and overall vibe. Do NOT change the person into a different face. Keep it clearly recognizable as the same person. Create one full-size ultra-high-quality 3D stylized character illustration, Pixar-inspired but original, based on the input person. Smooth plastic-like skin, soft rounded facial features, big expressive eyes, small nose, subtle blush (very minimal), cozy wholesome aesthetic. High-end character sculpting with stylized proportions while maintaining the real person’s likeness. 👕 Outfit / Costume (MUST MATCH INPUT) Keep the costume/outfit EXACTLY the same as the input image. Do not change colors, fabric type, accessories, layers, patterns, logos, or fit. No added glasses, no headphones, no new jacket, no new styling. 💇 Hair (Exact Match) Hair must remain the same as the input image: same hairstyle, same length, same hairline, only converted into clean stylized 3D hair shapes. 🎨 Render Quality Premium character sculpting, soft studio lighting, global illumination, subsurface scattering, soft shadows, cinematic depth of field, crisp edges. Octane/Arnold render look, ultra-clean, high-quality shading, 8K detail. 🎯 Composition Single full-size image (NOT a grid). Full-body or medium shot matching the input pose and vibe. Minimal clean studio background (solid color), no clutter. - Negative Prompt:
No outfit change, no costume change, no new clothes, no extra accessories, no glasses, no headphones, no makeup, no cosmetics, no lipstick, no eyeliner, no facial redesign, no different face, no extra limbs, no deformed hands, no scary look, no photoreal skin pores, no wrinkles, no blur, no noise, no watermark, no logo, no text. - Aspect ratio: Maintain the aspect ratio of the input image or default to 9:16.
- Reference Image:
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 · 63 lines · 35 tokens per session scan B 961580bddcb9
muapi-cartoon-dance-animation is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,263 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 1,087 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). 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
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-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
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-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.