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media (Library) overview
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media (Library)
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media (Library)
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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.
documents
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- specialization:media·SpecializationMedia Processing
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- page:index·PageAgentic AI Atlas Wiki