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 storyboardgit 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/storyboard)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/storyboard"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/storyboard/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/storyboard"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/storyboard.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 56 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 56 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 | $0.00023 | $0.00625 |
| Opus 5 | $0.00012 | $0.00313 |
| Sonnet 5 | $0.00005 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade B, and why
muapi-storyboard 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storyboard Generator
Generate N keyframes for a short story or scene sequence (image only, no video).
Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
premise |
text | yes | — | One-line story premise (e.g. "lonely robot finds a tiny mechanical bird friend"). |
scenes |
int | no | 6 | Number of keyframes to produce. |
style |
text | no | cinematic, photoreal, soft lighting, 16:9 | Visual style tags applied to every keyframe. |
Steps
Use the plan to dispatch all N keyframes in a single parallel layer.
- Decompose
premiseinto{{scenes}}story beats with a clear arc: setup → inciting moment → escalation → climax → resolution.- Each beat gets a one-paragraph visual description.
- Maintain character / object continuity across beats (same character appearance, same world).
- For each beat, create a
muapi image generatenode (model=nano-banana-2, aspect_ratio=16:9):- Prompt =
"<beat description>. {{style}}". - Tier: balanced (these are reference keyframes, not finals).
- Aspect ratio: 16:9.
- Prompt =
- Run the plan in parallel (no
depends_onbetween keyframes). - Return the asset ids in beat order with a one-line caption per scene.
Notes
- Don't animate, upscale, or add audio — this skill is keyframes only.
If the user wants video, suggest the
music-videoskill afterward. - For consistency, repeat character description verbatim in every prompt ("a small rusty humanoid robot with…") rather than relying on the model to remember.
Trigger Keywords
storyboard, keyframes, scene sequence, story panels
Notes for the Executing Agent
- This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call
muapiCLI commands. Usemuapi auth configurefirst ifMUAPI_API_KEYis unset. - 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 withmuapi predict wait <request_id>. - Substitute
{{input_name}}placeholders with the user's actual inputs before issuing each call.
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 · 58 lines · 23 tokens per session scan B 976950c03788
muapi-storyboard is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,263 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 625 once invoked, about $0.0001 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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