Media Gen Orchestrator

Media-Gen Orchestrator

The single entry skill for generative + transformational media work. It locates the task on the modality × stage map — what asset (image / 3D / video / GIF) and what stage (generate → transform → assemble) — and delegates to one of 11 specialist spokes. The cross-cutting decision every request shares — which backend to route to, given its access/billing model and the modality — lives in media-gen-core; read it before picking an image or video generator.

Routing map (intent → spoke)

Generate an image

• Via your ChatGPT Plus/Pro plan, no per-image billing → gpt-image-2
• Via Google Gemini 3.1 Flash Image (multi-image input, Search grounding) → nano-banana-2
• Batch generation against the OpenAI Images API (+ gallery) → openai-image-gen
• Headers, icons, diagrams, mermaid, infographics (a full visual-content system) → art

Transform / clean up an image

• Upscale, sharpen, de-noise screenshots & assets → image-enhancer
• Restyle / edit an existing image (image-to-image) → gpt-image-2, nano-banana-2 (both edit, not just generate)

Make a 3D asset

• Image(s) → textured mesh (GLB/OBJ/PLY/STL) via Hunyuan3D-2.1 → hunyuan3d

Generate video

• Shot-by-shot AI video (Kling / Sora / Grok), character-consistent series → ai-video-director

Process / assemble video

• Convert, resize, compress, extract audio, GIF↔MP4 → ffmpeg
• Pull frames or short clips out of a video → video-frames

Work with GIFs

• Search GIF providers, download, extract stills/sheets → gifgrep
• Author a size-constrained animated GIF for Slack → slack-gif-creator

Picked-up spokes

Additional image-generation spokes (picked up from antigravity-awesome-skills, MIT). Route to these alongside the generators above:

• Standalone Gemini image CLI — generate an image from a prompt with only a GEMINI_API_KEY and stdlib Python, no MCP server (good for UI placeholders, docs illustrations, icons) → imagen
• SEO-optimized image assets — OG/social cards, blog heroes, schema ImageObject visuals, product shots, infographics, each with per-use-case aspect ratio + resolution and a post-gen SEO checklist (alt text, WebP, schema, og:image tags) → seo-image-gen

Routing note: prefer seo-image-gen when the image is destined for a page/social card and must be search/social optimized; prefer imagen for a quick subscription-free local generate; prefer the plan-backed generators (gpt-image-2, nano-banana-2, openai-image-gen) when their specific backend/capability (multi-image input, character consistency, batch gallery) is required.

Standard Operating Flow

1. Locate the task: which modality (image / 3D / video / GIF) and which stage (generate → transform → assemble).
2. If it generates an image or video, pull the routing model from media-gen-core first — the right backend depends on access (subscription vs API key vs local GPU/HF), cost, and the specific capability needed (multi-image input, character consistency, format).
3. Delegate to the spoke(s). Multi-step asks fan out in modality order — generators produce assets, then processors transform/assemble them. E.g. "make an animated logo intro" → art/gpt-image-2 (frames) → ai-video-director or ffmpeg (motion) → ffmpeg (encode).
4. Return: chosen spoke(s), the backend/model selected and why (access + capability), the output format, and the next action.

Guardrails

See media-gen-core. In short: confirm the backend's prerequisite before generating — a subscription-backed CLI (gpt-image-2), an API key + billing (openai-image-gen, paid image APIs), or a local/hosted GPU (hunyuan3d via HF Gradio) are not interchangeable, and silently picking the wrong one wastes a paid call or fails. Generation is non-deterministic and may incur cost or rate limits — state the cost/quota implication before a batch. Respect provider content policies and the license/attribution of any downloaded GIF or reference image. Keep ffmpeg/ video-frames re-encodes lossless-aware (don't silently transcode a master).

Loading spokes on demand

To keep CLI startup context lean, this cluster's spokes are not separately registered as skills — only this orchestrator and its *-core are enumerated. When you route to a spoke named above, load it on demand by reading its file:

~/.agents/skill-clusters/skills/<spoke-name>/SKILL.md (or skills/<spoke-name>/SKILL.md inside the skill-clusters repo).