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-nano-bananaWrote 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-nano-banana)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/muapi-nano-banana"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-nano-banana/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-nano-banana"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-nano-banana.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 | $0.00037 | $0.00900 |
| Opus 5 | $0.00018 | $0.00450 |
| Sonnet 5 | $0.00007 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
muapi-nano-banana 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-nano-banana — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🍌 Nano-Banana Expert Skill (Gemini 3 Style)
A specialized skill for AI Agents to leverage "Reasoning-Driven" image generation. Based on the advanced prompting architecture of Google's Gemini 3 (Nano Banana Pro), this skill moves beyond keyword stuffing to structured, logic-based creative briefs.
Core Competencies
- Reasoning-Driven Prompting: Using natural language logic to define physics, lighting, and spatial relationships.
- Structured Creative Briefs: Implementing the "Perfect Prompt" formula:
Subject + Action + Context + Composition + Lighting. - Text Rendering Precision: Explicitly defining typography and signifiers for legible text integration.
- Contextual Grounding: Using "Search Grounding" logic (simulated) to anchor generations in real-world accuracy.
🏗️ Technical Specification
1. The "Perfect Prompt" Formula
| Component | Description | Example |
|---|---|---|
| Subject | Detailed entity description | "A stoic robot barista with exposed copper wiring" |
| Action | Dynamic interaction | "Pouring a latte art leaf with mechanical precision" |
| Context | Environment & Atmosphere | "Inside a neon-lit cyberpunk cafe at midnight" |
| Composition | Camera & Lens choice | "Close-up, 85mm lens, f/1.8 aperture" |
| Lighting | Mood & Direction | "Volumetric blue rim light, warm cafe glow" |
| Style | Aesthetic anchor | "Cinematic, photorealistic, 4K production value" |
2. Advanced Features
- Negative Constraint Logic: Instead of "no blurry," use "Ensure sharp focus on the subject's eyes."
- Identity Consistency: (Simulated) "Maintain consistent facial structure across variations."
- Text Integration: Use double quotes for specific text:
The sign reads "OPEN 24/7".
🧠 Prompt Optimization Protocol (Agent Instruction)
Before calling the script, the Agent MUST rewrite the user's prompt into a logic-driven Reasoning Brief:
- NO KEYWORD SOUP: Remove "8k, masterpiece, ultra-detailed." Use full, descriptive sentences.
- PHYSICAL CONSISTENCY: Describe how elements interact (e.g., "The light from the crystal shards casts caustic patterns across the obsidian floor").
- TEXT PRECISION: If the user wants text, define it precisely:
featuring a sign that says "STORE NAME" in a weathered serif font. - OPTICAL DIRECTIVES: Specify lens behavior: Shallow Depth of Field (f/1.8), Macro Lens, Anamorphic Flare.
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 · 81 lines · 37 tokens per session scan A e26187b5a7c6
muapi-nano-banana is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,263 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 900 once invoked, about $0.0002 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.
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-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.