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Media gen core

Skill Sheshiyer/skill-clusters/skills/media-gen-core

Shared reference for the media-gen cluster: the backend-routing decision (access/billing model × modality), the generate→transform→assemble pipeline, output-format conventions, and the cross-tool guardrails. USE WHEN choosing an image/video/3D/GIF backend, planning a multi-stage media pipeline, or reasoning about cost, format, or provider policy.From its SKILL.md

Install
npx -y skills add Sheshiyer/skill-clusters --skill media-gen-core

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SKILL.md

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Media-Gen Core

Shared model for the media-gen cluster. The generators and processors all hang off one decision and one pipeline — keep them consistent here so no spoke contradicts another.

1. The routing decision (this cluster's defining choice)

Every generative request resolves on two axes: modality (what asset) × access model (what it costs / what it needs to run). Pick the cell, then the spoke.

Request ──> [ modality: image | 3D | video | GIF ]
        ──> [ access:  subscription-CLI | API-key+billing | local/hosted-GPU | free-search ]
        ──> spoke
  • Subscription-CLI — runs through an existing consumer plan, no per-asset billing. gpt-image-2 (your ChatGPT Plus/Pro via the Codex CLI). Cheapest marginal cost; rate-limited by the plan.
  • API-key + billing — a provider key, billed per image/call. openai-image-gen (OpenAI Images API, OPENAI_API_KEY), and paid image/video APIs in general. Predictable, scalable, costs money per call — state batch size before running.
  • Local / hosted-GPU — model runs on a GPU you reach, not a metered text-prompt API. hunyuan3d (Tencent Hunyuan3D-2.1 via a Hugging Face Gradio endpoint). No per-call billing, but subject to the endpoint's availability/queue.
  • CLI-inference / groundednano-banana-2 (Gemini 3.1 Flash Image via the infsh CLI; multi-image input up to 14, Google-Search grounding) sits between subscription and API depending on how it's keyed.
  • Free-search / processing — no generation at all: gifgrep (search & download), ffmpeg / video-frames (deterministic local transforms), image-enhancer (upscale). Zero model cost.

Rule: confirm the chosen backend's prerequisite (plan / key / GPU endpoint) is present before generating; never silently swap one access model for another — a failed or mis-billed call is the failure mode this cluster exists to prevent.

2. The pipeline (how spokes compose)

GENERATE ──> TRANSFORM ──> ASSEMBLE/DELIVER
 image/3D/video      enhance · restyle · extract      encode · size-fit · gallery

Generators emit raw assets; processors refine and package them. A request that needs motion or a final container almost always ends in ffmpeg. Examples:

  • Still → upscaled still: gpt-image-2/nano-banana-2/openai-image-genimage-enhancer.
  • Photo → 3D: reference image (any image spoke) → hunyuan3d → GLB.
  • Concept → clip: ai-video-director (shots) → ffmpeg (encode/resize) ; pull stills with video-frames.
  • Reaction GIF: gifgrep (find/extract) or slack-gif-creator (author) → ffmpeg (GIF↔MP4, size fit).

3. Backend / modality matrix

ModalitySpokeBackendAccess modelOutput
Image (gen)gpt-image-2GPT Image 2 (ChatGPT Images 2.0)subscription-CLI (ChatGPT Plus/Pro)PNG, edits, refs
Image (gen)nano-banana-2Gemini 3.1 Flash Imageinfsh CLI (multi-image, grounded)images, multi-input edits
Image (gen, batch)openai-image-genOpenAI Images APIAPI key + billingPNG + index.html gallery
Image (system)artmixed (icons, diagrams, mermaid)provider keys in agent .envheaders, icons, diagrams
Image (transform)image-enhancerupscalerlocal / no model costsharpened/upscaled image
3Dhunyuan3dHunyuan3D-2.1hosted GPU (HF Gradio)GLB / OBJ / PLY / STL
Video (gen)ai-video-directorKling 3.0 · Sora · Grokper-tool (key/plan)shot-by-shot prompts → clips
Video (process)ffmpegFFmpeglocal CLIconvert/resize/compress/audio
Video (extract)video-framesFFmpeglocal CLIframes / short clips
GIF (find)gifgrepGIF providersfree search/downloadGIFs, stills, sheets
GIF (author)slack-gif-creatorcomposable primitiveslocalSlack-size-validated GIF

4. Output & convention notes

  • Formats: 3D → GLB is the portable default (OBJ/PLY/STL on request). Video masters stay in a lossless/high-bitrate container; only down-encode at the delivery step. GIF for Slack must pass the size validators in slack-gif-creator.
  • Determinism: ffmpeg, video-frames, image-enhancer, gifgrep are reproducible; the generative spokes are not — same prompt ≠ same output. Capture the prompt/seed when one matters.
  • Reference images (image-to-image / multi-ref / image-to-3D) are first-class inputs across gpt-image-2, nano-banana-2, and hunyuan3d — feed an asset from an earlier stage rather than re-describing it.

5. Shared guardrails

  • Prerequisite before spend: verify plan/key/GPU endpoint exists before a generative call.
  • State the cost/quota of any batch or paid API run up front.
  • Don't swap access models silently — subscription, API-billing, and local are not equivalent.
  • Respect provider content policy and the license/attribution of downloaded GIFs and reference images.
  • Preserve masters: re-encode lossless-aware; never transcode the source in place.
  • Generative output is non-deterministic — record prompt/seed when reproducibility matters.

What ships with it

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Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most media documents skills give in ~1.5k tokens

Counted across 157 of the 158 authors here whose files we hold, read 2026-08-07

  • Provide posting time recommendationsin 7 of 157, across 5 files
  • Track metrics over time to identify trendsin 6 of 157, across 2 files
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  • Download top-K results with an attribution sidecarin 5 of 157, across 2 files

Said here and by no other author read

  • resolve modality and access model before choosing a backend
  • confirm prerequisites exist before any generative call
  • state cost or quota up front for paid API runs
  • follow the generate transform assemble pipeline
  • use reference images as first-class inputs
  • use GLB as the default format for 3D assets

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