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Ztp3d Cinematic Postfx Camera Lens Suite

ASecurity

Depth of Field, Lens and Grain: specialized Perfect_AI quality workflow for Cinematic Post-Processing Studio.

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  • Added November 7, 2026
designgitsecurity

Security analysis

A100/100

Scanned November 7, 2026

npx -y skills add Realjustawall/Perfect-AI --skill ztp3d-cinematic-postfx-camera-lens-suite --agent claude-code

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SKILL.md
---
name: ztp3d-cinematic-postfx-camera-lens-suite
description: "Depth of Field, Lens and Grain: specialized Perfect_AI quality workflow for Cinematic Post-Processing Studio."
---
# Depth of Field, Lens and Grain

## Where this skill applies
Layer focus, vignette, chromatic aberration, film grain and procedural dirt thoughtfully.

## Task-specific implementation plan
Use physically motivated lens settings. Do not blur critical content, and disable expensive/full-screen effects on low devices. Some effects need depth buffers.

### Inputs and dependencies
- Existing project source and its installed versions, target browser, 3D scene/camera, ownership rules.
- Parent integration guide: `../ztp3d-cinematic-postfx/SKILL.md`.
- Working example and adapter: `../../src/adapters/postprocessing.mjs`.
- Upstream technical reference: https://github.com/pmndrs/postprocessing.

### Step-by-step operational checklist
1. Inspect target source files and preserve all existing behavior; explicitly name the owner of each component, canvas and animation timeline.
2. Validate input data and preconditions. For missing support, stop gracefully with a useful error and a visible fallback.
3. Implement depth of field, lens and grain through smallest observable patch, preferring local modules, deterministic data and reusable functions.
4. Include edge cases: empty assets, large assets, stale asynchronous work, component unmount, 320px mobile, RTL layout and prefers-reduced-motion.
5. Verify each change independently before enabling it in the full pipeline; record precise reproduction steps.
6. Test final rendering on browser and target device and collect screenshot, console and resource lifecycle evidence.
7. Classify final result as documented / implemented / unit tested / browser tested / actual GPU tested.

### Targeted acceptance tests
Screenshot foreground/background focus, edge artifacts and reduced-motion modes.

### Failure and rollback
- If a feature is unavailable, keep prior renderer or accessible HTML content intact and report why.
- If perf regresses, turn off the optional effect/plugin through an explicit feature flag.
- Do not change or delete upstream Perfect_AI skills; add new files or narrowly patch the destination application only on request.
- Always distinguish simulated/browser/device evidence.

### Example deliverables
- Implementation diff and dependency changes; deterministic fixture and tested commands.
- Baseline/target render screenshots and p95 frame-time notes where GPU work is involved.
- List of remaining limitations, security/privacy and licensing issues.

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