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-cinema-directorWrote 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-cinema-director)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/muapi-cinema-director"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-cinema-director/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-cinema-director"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/muapi-cinema-director.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- 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 | $0.00040 | $0.01066 |
| Opus 5.5 | $0.00016 | $0.00426 |
| Sonnet 5.5 | $0.00008 | $0.00213 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
muapi-cinema-director 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-cinema-director — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🎬 AI Cinema Director Skill
A specialized skill for AI Agents to direct high-fidelity cinematic video. The Cinema Director skill translates high-level creative intent into technical cinematographic directives for state-of-the-art video models (Veo3, Kling, Luma).
Core Competencies
- Shot Composition Analysis: Mapping emotional beats to appropriate framing (e.g., Extreme Close-Up for intimacy, Wide Shot for isolation).
- Camera Movement Orchestration: Directing complex physical movements (Dolly, Truck, Crane) and lens-based effects (Rack Focus, Dolly Zoom).
- Lighting & Atmosphere Design: Specifying temporal and stylistic lighting (Golden Hour, Chiaroscuro, Volumetric God Rays).
- Technical Parameter Optimization: Automatically selecting optimal frame rates, aspect ratios, and model-specific biases.
🏗️ Technical Specification
1. Intent Mapping Table
| Creative Intent | Framing | Movement | Lighting |
|---|---|---|---|
| Heroic Reveal | Low Angle / Wide | Crane Up / Orbit | Rim Lighting / High Contrast |
| Tense/Uneasy | Dutch Angle | Handheld Shake | Low Key / Harsh Shadows |
| Introspective | Close-Up | Slow Push In | Soft Rembrandt / Window Light |
| Majestic/Epic | Extreme Wide | Drone Flyover | Golden Hour / Volumetric |
| Melancholic | Profile / Medium | Slow Pull Out | Blue Hour / Desaturated |
2. Physical Camera Movements
Dolly In/Out: Physical camera movement on a track toward/away from the subject.Truck Left/Right: Lateral physical movement.Crane/Jib: Sweeping vertical movement from a height.Orbit: Circular movement around a center point.Pedestal: Vertical elevation change (without tilting).
3. Lens & Optical Controls
Shallow DOF: Background blur (Bokeh).Anamorphic: Horizontal flares and wide-screen cinematic feel.Rack Focus: Shifting focus between planes within the shot.
🧠 Prompt Optimization Protocol (Agent Instruction)
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 · 94 lines · 40 tokens per session scan A 24a884942a0b
muapi-cinema-director is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (25 stars, last pushed 28d ago), licensed MIT. It adds 40 tokens to every session and 1,066 once invoked, about $0.0002 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-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.