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

Asset Director - Cinematic Pipeline

When To Use

This stage prepares the usable media for the final cinematic edit: source selects, title-card assets, optional support inserts, music, ambience, and subtitle assets when needed.

Animation authoring for cinematic titles and overlays

Before authoring title cards, name plates, or SVG overlays, read skills/meta/animation-runtime-selector.md for runtime routing. Cinematic pieces lean on a handful of high-craft motion patterns:

Cinematic need	Recommended approach
Hero title with subtle reveal	Remotion HeroTitle component (existing)
Logo build / cinematic sting on SVG	GSAP DrawSVG + MotionPath — read .agents/skills/gsap-plugins/SKILL.md
Curved camera move across a wide still or overlay	GSAP MotionPath — read .agents/skills/gsap-plugins/SKILL.md
Per-character title reveal (prestige / trailer style)	GSAP SplitText — read .agents/skills/gsap-plugins/SKILL.md
Cinematic easings (Unreal-style, stuttering, weighted)	GSAP CustomEase / EasePack — read .agents/skills/gsap-plugins/SKILL.md
Name plate lower-third with elastic settle	Remotion spring() is usually enough; GSAP CustomEase if you need stutter
Film grain / particle overlay	Remotion ParticleOverlay (existing)
Color grade / LUT	tools/enhancement/color_grade.py (not an animation concern)

Cinematic is where GSAP earns its weight most often — the genre rewards crafted easings and precise curved motion that primitive interpolate() struggles to express cleanly. Don't over-use it either: for a fade-in title, Remotion spring() still beats a whole GSAP dependency.

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["proposal"]["proposal_packet"]	Scene intent and beat plan
Tools	subtitle_gen, audio_enhance, image_selector, video_selector, pixabay_music (free, default), freesound_music (free), music_gen (ElevenLabs, paid) — selectors auto-discover all available providers from the registry. Default to pixabay_music before reaching for music_gen.	Optional support asset creation
Playbook	Active style playbook	Brand and typography consistency

Process

1. Prioritize Source Selects

Start with:

• source footage selects,
• stills,
• title-card backgrounds,
• any approved provided music or ambient beds.

These are the primary materials. Everything else is support.

If proposal_packet.metadata.motion_required = true, actual moving footage or generated video clips are mandatory. In that case:

• stills may be used only as reference material or backing elements inside a larger motion composition,
• stills may not replace the planned motion shots,
• a still-image teaser is not an acceptable fallback unless the user explicitly approves an animatic.

1b. Sample Preview (Prevents Wasted Spend)

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

1. Generated insert sample (if using image_selector or video_selector): Generate one representative visual. Confirm it complements the source footage before batching.
2. Music sample (try pixabay_music first — free, searchable by mood/BPM; fall back to freesound_music for cues and ambience; only reach for music_gen when the search tools miss the brief): sample or retrieve a short clip. Confirm mood and energy match the beat plan.

If motion_required = true, the representative visual must be a video clip sample, not a still image sample.

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

Before the sample is generated, tell the user exactly which generation path will be used:

• tool,
• provider,
• model or variant,
• generation mode,
• why it was selected.

If that path fails, stop and ask before trying a different provider, model, or generation mode.

2. Generate Support Assets Only Where Needed

Optional generated assets should fill clear gaps:

• missing transitional b-roll,
• concept-led inserts,
• texture or atmosphere cards,
• simple textural motion backgrounds.

For motion-required jobs, use video_selector first for generated shots. image_selector may support look development, concept frames, or embedded design layers, but it does not satisfy the motion requirement by itself.

3. Prepare A Real Audio Plan

Store:

• chosen music track or prompt,
• ambience layers,
• impact or transition sounds,
• subtitle assets if dialogue or narration is present.

4. Use Metadata For Rights And Intent

Recommended metadata keys:

• source_selects
• music_plan
• ambience_plan
• title_assets
• generated_support_assets
• rights_notes

Pre/Post Self-Review for Generation Prompts

Before sending a prompt to any image or video generation tool, run a three-step self-review modeled on the CHAI oversight loop ("Building a Precise Video Language with Human-AI Oversight", arXiv 2604.21718v2). Cost is small (no extra tool calls); benefit is large (avoids wasted generations). For cinematic, this matters most for hero-frame prompts — one bad hero frame ruins the piece, and hero frames are the most expensive shots to regenerate.

Step 1 — Pre-caption pass. Write the prompt the way you'd write it today. Do not over-edit; aim for a complete first draft.

Step 2 — Critique pass. Score the draft against the 5-aspect checklist (Subject / Subject Motion / Scene / Spatial Framing / Camera). For each aspect:

• Is it specified? If not, is the omission deliberate (e.g., "no subject — scenery shot") or accidental?
• Are confusable terms disambiguated? (dolly vs zoom, pan vs truck, bird's-eye vs aerial, fisheye vs barrel, full shot vs close-up)
• Are emotional adjectives ("epic", "moody", "cinematic") replaced with their visual causes (low-key lighting, slow push-in, anamorphic flare, deep shadows)?
• For multi-shot prompts and identity-anchored hero frames: is identity anchored verbatim across shots?

Step 3 — Post-caption pass. Rewrite filling the missing aspects, fixing confusable terms, and replacing subjective language. The post-caption is what gets sent to the generation tool.

Log the (pre, critique, post) triplet in the asset metadata for traceability. This mirrors the CHAI workflow and creates a record the reviewer can audit.

5. Quality Gate

• source and support assets are clearly distinguished,
• generated inserts are limited and purposeful,
• audio plan matches the beat map,
• every referenced file exists.
• if motion is required, the asset set contains actual video clips for the motion-led beats.

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

• Generating extra shots before proving the source edit works.
• Treating music as a single loop instead of a beat-aware element.
• Forgetting rights or provenance notes for supplied assets.
• Quietly downgrading from video clips to still images because one provider or renderer failed.
• Quietly switching providers or models after the user approved a generation path.

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.