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 selfie-with-celebritiesgit 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/selfie-with-celebrities)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/selfie-with-celebrities"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/selfie-with-celebrities/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/selfie-with-celebrities"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/selfie-with-celebrities.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 63 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 63 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 | $0.00047 | $0.00902 |
| Opus 5 | $0.00023 | $0.00451 |
| Sonnet 5 | $0.00009 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade B, and why
muapi-selfie-with-celebrities 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 9d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Selfie with Celebrities
Generate a realistic behind-the-scenes selfie of the user with a celebrity or main actor from a specific movie, followed by an option to generate a cinematic long-take video connecting multiple selfies.
Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
movie_name |
text | yes | — | The name of the movie featuring the celebrity you want to take a selfie with (e.g., "Harry Potter", "The Matrix"). |
user_image |
image_url | yes | — | A clear photo of the user to be used for the selfie. |
Steps
Phase A — Generate First Selfie Image
If the {{user_image}} is not provided, ask the user to upload their image first.
Once provided, submit the plan with ONE step:
- Selfie Image Generation —
muapi image edit(model=nano-banana-2-edit):- Reference Image:
{{user_image}} - Prompt:
POV: close up shot, A realistic photo of the person (don't change the person's original clothes) holding a black iPhone 16 to take a picture with the main actor from the movie "{{movie_name}}" on a movie set with movie scene background. Behind-the-scenes atmosphere, including film equipment, film cameras, filled with props, and busy crew members. Sharp focus on the characters. bright scene. Aspect ratio 9:16. - Aspect ratio: 9:16
- Reference Image:
After generating the first image:
- Present the generated selfie to the user.
- Ask the user if they would like to create a second, third, etc. image with different movies or actors.
- Also, suggest to the user that once they have multiple images, you can create a seamless cinematic video transitioning between the selfies.
Phase B — Generate Connecting Video (Only when requested)
If the user has generated at least two selfies and asks to create a video connecting them, submit the plan with ONE step:
- Cinematic Video Generation — Use an image-to-video model like
kling-o1-image-to-video,pixverse-v5.5-i2v, orveo3.1-image-to-video.- First frame image: The first generated selfie.
- Last frame image: The second generated selfie (or use multiple steps if connecting more than two).
- Prompt:
A seamless cinematic long-take. The camera follows a person as she finishes a photo with an actor, then she naturally turns and walks toward the right. The camera tracks her movement with a smooth gimbal-like motion. Her gait is consistent and confident. Upon entering a new behind-the-scenes movie set, she slows down and stops gracefully, raising her phone with a bright smile to take a selfie with two actors. The scene transition is natural and smooth, with a seamless connection. - Aspect ratio: 9:16
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.
- 9d ago First seen · 65 lines · 47 tokens per session scan B 1c18a49f5e5b
muapi-selfie-with-celebrities is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,263 stars, last pushed 3d ago), licensed MIT. It adds 47 tokens to every session and 902 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 2026-09-03.
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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.