video/skills/pipelines/hybrid/asset-director.md

Asset Director - Hybrid Pipeline

When To Use

This stage prepares the support kit around the anchor edit: subtitles, diagrams, generated inserts, narration, music, and reusable overlay systems.

Prerequisites

Layer	Resource	Purpose
Schema	schemas/artifacts/asset_manifest.schema.json	Artifact validation
Prior artifacts	state.artifacts["scene_plan"]["scene_plan"], state.artifacts["script"]["script"], state.artifacts["idea"]["brief"]	Support needs and variant plan
Tools	subtitle_gen, tts_selector, image_selector, video_selector, diagram_gen, code_snippet, music_gen, audio_enhance — selectors auto-discover all available providers from the registry	Optional support asset production
Playbook	Active style playbook	Consistency rules

Process

1. Build Shared Support Assets First

Start with reusable systems:

• subtitle treatment,
• lower-third or label system,
• stat-card system,
• CTA container,
• diagram style.

1b. Sample Preview (Prevents Wasted Spend)

Before batch-generating support assets, produce one sample of each expensive generated type and show the user:

1. TTS sample (if narration is needed): Generate one section. Confirm voice and tone before batching.
2. Image/video sample (if generating inserts): Generate one representative visual. Confirm style fits the source footage before batching.

If rejected, adjust parameters and retry (max 3 iterations). Do not batch until approved.

2. Generate Only The Support Assets You Need

Support assets should fill identified needs from the script and scene plan, not speculative possibilities.

3. Preserve Anchor Truth

Keep the metadata clear about which assets are:

• source-derived,
• provided,
• recorded,
• generated.

4. Use Metadata For The Support Map

Recommended metadata keys:

• shared_support_assets
• scene_asset_index
• source_vs_generated_map
• variant_assets

5. Quality Gate

• support assets map to real narrative needs,
• reusable kits are present,
• source and generated assets are clearly separated,
• every referenced file exists.

Mid-Production Fact Verification

If you encounter uncertainty during asset generation:

• Use web_search to verify visual accuracy of subjects (e.g. what does this building actually look like?)
• Use web_search to find reference images before generating illustrations
• Log verification in the decision log: category="visual_accuracy_check"

Visual accuracy matters. If the script mentions a specific place, person, or object, verify what it actually looks like before generating images. Don't rely on the AI model's training data — it may be wrong or outdated.

Common Pitfalls

• Overbuilding support assets before the anchor cut is proven.
• Losing track of which assets are generated versus supplied.
• Creating inconsistent overlay systems across one project.

When You Do Not Know How

If you encounter a generation technique, provider behavior, or prompting pattern you are unsure about:

1. Search the web for current best practices — models and APIs change frequently, and the agent's training data may be stale
2. Check .agents/skills/ for existing Layer 3 knowledge (provider-specific prompting guides, API patterns)
3. If neither helps, write a project-scoped skill at projects/<project-name>/skills/<name>.md documenting what you learned
4. Reference source URLs in the skill so the knowledge is traceable
5. Log it in the decision log: category: "capability_extension", subject: "learned technique: <name>"

This is especially important for:

• Video generation prompting — models respond to specific vocabularies that change with each version
• Image model parameters — optimal settings for FLUX, DALL-E, Imagen differ and evolve
• Audio provider quirks — voice cloning, music generation, and TTS each have model-specific best practices
• Remotion component patterns — new composition techniques emerge as the framework evolves

Do not rely on stale knowledge. When in doubt, search first.