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# media Generative media point tasks — one per modality and operation — plus one end-to-end production pipeline that carries a brief through to published assets and post-publish metrics. ## Point tasks - `image-generation.js` (`@process specializations/media/image-generation`) — Image-generation persona. Parse creative brief -> select optimal model (Imagen 3/4, Flux, DALL-E, Stable Diffusion) -> generate variants in parallel -> validate technical + creative quality -> organise outputs with metadata. - `image-editing.js` (`@process specializations/media/image-editing`) — Image-editing persona. Analyse source + operation request -> select tool (Imagen Edit, DALL-E Edit, Stability Edit, Photoshop AI, Upscaler) -> apply operation (inpaint | outpaint | object-removal | background-replace | style-transfer | upscale) -> validate edge quality / color consistency / artifact absence. - `video-generation.js` (`@process specializations/media/video-generation`) — Video-generation persona. Parse request (text-to-video | image-to-video | video-to-video) -> select optimal model (Veo 2/3, Luma, RunwayML, Stable Video, Minimax) -> generate via MCP GenMedia with camera/lighting/composition parameters -> validate technical + content quality -> retry with fallback model on low-quality outputs. - `video-editing.js` (`@process specializations/media/video-editing`) — Video-editing persona. Analyse source + request -> select tool (Veo Edit, FFmpeg AI, DaVinci Resolve, RunwayML Edit, Video Enhance) -> run per-op pipeline (temporal-inpaint | stabilise | color-grade | upscale | transitions | scene-cut | audio-sync) -> validate frame consistency + audio sync. - `speech-generation.js` (`@process specializations/media/speech-generation`) — Speech-generation persona. Analyse text + voice requirements (language, style, emotion, SSML) -> select model (Chirp 3, Azure Speech, ElevenLabs, OpenAI TTS, AWS Polly) -> synthesise via MCP GenMedia -> validate naturalness / pronunciation / audio specs. - `music-generation.js` (`@process specializations/media/music-generation`) — Music-generation persona. Parse composition brief (genre/mood/duration/instruments) -> select optimal model (Lyria, MusicLM, AIVA, Mubert, Amper) -> generate via MCP GenMedia -> apply mastering + stem separation if requested -> validate musical coherence + technical audio. ## Pipeline `media-production-pipeline.js` (`@process specializations/media/media-production-pipeline`) — End-to-end media production: brief -> research -> script -> produce -> review gates (editorial, legal, brand) -> publish -> measure. Its task ids, in execution order: | Task id | Kind | Role | |---|---|---| | `media.research` | agent | Research the brief | | `media.script` | agent | Script/outline from the research | | `media.produce` | agent | Produce the asset | | `media.review-gate` | breakpoint | One pass per gate (`editorial`, `legal`, `brand`), up to 3 attempts each | | `media.publish` | agent | Publish to each entry in `publishTargets` (in parallel) | | `media.measure` | agent | Collect 24h post-publish metrics | ## Known gap (OPEN) The pipeline's review gates are plain `ctx.breakpoint` calls, **not** `routedBreakpoint` policy gates: there is no `policy-gated` tag, no actionId, and no `gatedActions` audit record of the decision. Worse, each gate is skipped outright when the matching `reviewers[gate]` input is absent, and `media.publish` carries no approval gate of its own — so a run with no `reviewers` publishes with zero human review. This is stated here as an **open gap, not a fixed behaviour**; adding routed policy gates is a separate change and is not done in this pass. ## Assets - Skill: [`skills/generative-media-prompting/SKILL.md`](./skills/generative-media-prompting/SKILL.md) — the brief-to-prompt convention shared by the six point tasks. - There are no agents in this specialization. --- Descriptions in this README are transcribed from the files' own `@description` headers, not invented.
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