Generative Media Skills

Quick Verdict

Generative Media Skills is an MIT-licensed skill and workflow repository maintained by SamurAIGPT for agents such as Claude Code, Cursor, Gemini CLI, and OpenCode. It organizes image, video, and audio work into core primitives, an expert library, and multi-step recipes. It is useful for technical creators who want an agent to orchestrate product images, storyboards, short videos, music, edits, and campaign assets from reviewable instructions.

Keep three layers separate. This repository primarily contains skill instructions, shell scripts, schema data, and recipes. muapi-cli is a separate package that handles authentication, uploads, job submission, polling, downloads, and an MCP mode. Actual inference is performed by the paid muapi.ai API and its upstream models. Free repository code does not make generation free or grant rights to input media, faces, voices, brands, upstream model output, or commercial publication.

Best For

• Developers and creators who already have a MuAPI account and want agent-orchestrated multimedia pipelines.
• Marketing teams turning approved product media, brand constraints, and platform formats into reviewable recipes.
• Technical teams willing to audit shell scripts, npm or pip packages, skill prompts, and upstream model terms.
• It is not a fit for fully offline generation, material that cannot be uploaded, or projects without clear face, voice, trademark, copyright, and publication permissions.

Key Features

• Core and Library split: core wrappers handle upload, edit, authentication, and polling, while the library stores cinematography, branding, UI, and social-media expertise.
• Workflow recipes: SKILL.md files combine image, video, and audio calls into product ads, social packs, storyboards, try-on, and other multi-step workflows.
• MuAPI CLI delegation: scripts call the independent muapi-cli, using structured JSON, semantic exit codes, --jq, downloads, and asynchronous job handling.
• Broad media operations: documented API coverage includes image generation/editing, video generation, music, effects, upscaling, background removal, lip sync, and clipping.
• Local-media upload: local images, video, faces, and audio can be uploaded to a remote CDN/API for processing.
• Separate MCP mode: muapi mcp serve is a MuAPI CLI/service capability exposing structured tools to an MCP host; it is not an MCP server implemented by this skills repository.
• Agent installation: install all or selected skills with npx skills add, or clone the repository for OpenCode to load .opencode/skills.

Use Cases

A practical workflow pins muapi-cli, configures a low-privilege API key, and installs only the required skills. An agent can read a product-ad recipe, upload an approved source image, create keyframes, submit a video job, and download output into a controlled directory. Record the model, parameters, request ID, input hash, charge, and approval at each step so a final asset remains reproducible and auditable.

The command npx skills add SamurAIGPT/Generative-Media-Skills --all fetches a remote repository and writes into agent skill directories. Shell workflows can then invoke a globally available CLI. Review the target commit, scripts, SECURITY.md, and dependency graph first. Prefer cloning a pinned commit, selecting only necessary skills, and testing with a separate account or container. Skill text is untrusted code-adjacent input; it must not gain access to unrelated secrets, arbitrary install authority, or sensitive paths.

Local media is uploaded to muapi.ai/CDN infrastructure and potentially upstream processors. Before sending unreleased client work, children, employees, medical data, identity documents, faces, voices, or location-bearing files, establish purpose, region, retention, deletion, subprocessors, and training policy. Face-swap, celebrity, brand imitation, and style recipes require consent and rights review. Technical capability does not equal permission to publish or advertise.

Pricing

The skills repository is free under MIT and currently has no stable GitHub Release. Its skills, scripts, and schema do not include inference credits. muapi-cli can be installed through npm, pip, or npx, while muapi.ai model calls require an API key and are billed through credits or endpoint-specific pricing. Model inventory, price, latency, and regional availability can change; check the current service before each production run.

Without stable releases, a main-branch installation can change by commit. Pin the repository commit, exact CLI version, and model IDs. Regression-test cost, schema, upload behavior, output quality, and downloads before upgrading, while still monitoring security fixes.

Pros

• Skills and workflows are visible, allowing teams to inspect how creative intent becomes API actions.
• The Core, Expert Library, and recipe layers are easier to select and maintain than one large prompt.
• JSON output, --jq, exit codes, and asynchronous polling suit scripts and agents.
• Image, video, audio, and editing coverage reduces per-provider glue code.
• Multiple entry points support Claude Code, Cursor, Gemini CLI, OpenCode, and MCP hosts.
• Recipes can capture repeatable campaign and production decisions.

Cons

• Open-source skills do not include free inference; execution depends on MuAPI credits and upstream supply.
• Skills, MuAPI CLI, and the hosted service are separate boundaries that documentation can blur.
• No stable repository Release means the branch, schema, scripts, and CLI can drift.
• Local media leaves the device, introducing retention, subprocessors, training, and cross-border privacy concerns.
• Face, celebrity, music, brand, and style recipes can trigger publicity, voice, trademark, copyright, or platform-policy issues.
• Agent-executed shell scripts, package installation, uploads, and API-key management require isolation and approval.

Alternatives

FAQ

Does Generative Media Skills generate media by itself?

The repository mainly supplies skills, recipes, shell scripts, and schema. The separate muapi-cli submits and polls work, while muapi.ai and upstream services perform inference.

Does a free repository make every generation free?

No. MIT covers repository code. MuAPI model calls consume paid credits, and the consuming agent, storage, and human review may carry additional costs.

Is muapi mcp serve part of the skills repository?

It is a runtime mode of MuAPI CLI. The skills can teach an agent to use it, but the CLI/server and the SKILL.md collection are distinct software boundaries.

Is one-command installation safe?

Not by default. Inspect the commit, installer, scripts, and dependencies; pin versions; install only required skills; run in isolation; and block access to unrelated secrets and paths.

What should be checked before uploading faces, voices, or client assets?

Confirm subject consent, asset ownership, purpose, processing region, retention and deletion, subprocessors, training policy, and permitted publication scope.

Can generated output be used commercially?

There is no universal answer. Review input rights, prompt content, MuAPI and upstream model terms, face and brand permissions, music rights, and destination-platform policy. Keep an approval record.

Bottom Line

Generative Media Skills is valuable because it expresses multimedia production as agent-readable and team-reviewable skills and recipes, not because it supplies free models. Evaluate the open repository, MuAPI CLI, and paid service separately. Pin commits and package versions, review installation and shell authority, protect API keys, and place human gates around uploads, charges, media rights, and publication.