| name | media-gen-orchestrator |
| description | Route a media-generation task to the right tool among 11 specialists — image generation (GPT Image 2, Nano Banana 2, OpenAI Images), image enhancement, image-to-3D, AI video direction, FFmpeg processing, frame extraction, and GIF search/authoring. USE WHEN a user wants to generate, edit, upscale, convert, or assemble an image, 3D model, video, or GIF but hasn't named the specific tool or backend. |
| cluster | media-gen |
| version | 1.0.0 |
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
- Locate the task: which modality (image / 3D / video / GIF) and which stage (generate →
transform → assemble).
- 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).
- 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).
- 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).
