# Anime Live Action Pipeline

> 7-step anime-to-live-action conversion pipeline: analyze clip → design characters → generate references → plan shots → generate clips → review quality → assemble final film. Use for all anime-to-live-action generation requests.

- Skill: `johnsonhk88/anime-live-action-pipeline` (Agent Skill)
- Install (CLI): `npx skillmds@latest add johnsonhk88/anime-live-action-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/johnsonhk88/anime-live-action-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: johnsonhk88 (https://skillmd.com/u/johnsonhk88)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/johnsonhk88/anime-live-action-pipeline

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# Anime to Live-Action Pipeline

## Overview

This skill provides the standard workflow for converting Japanese anime clips into cinematic live-action short films using the Deep Agents framework.

## Pipeline Steps

### Step 1: Analyze

Delegate to `analyze-agent` with the user's anime clip path and mode setting.

Pass:
- `clip_path`: Path to the input anime video
- `mode`: The execution mode (local / cloud / hybrid)

The agent returns structured data about scenes, characters, motion, and art style.

### Step 2: Design Characters

Delegate to `design-character-agent` with the analysis result from Step 1.

Pass:
- The `characters` array from the analysis
- Any `style_notes` for guidance

The agent returns detailed live-action character sheets with appearance, wardrobe, and expression sets.

### Step 3: Generate References

Delegate to `generate-reference-agent` with the character designs and style notes.

Pass:
- Character design data
- Style notes and reference prompts from the design step

The agent returns paths to generated reference images.

### Step 4: Plan Shots

Delegate to `plan-shots-agent` with the scene analysis, style notes, and reference images.

Pass:
- Scene data from the analysis (scenes array)
- Style notes
- Reference image paths

The agent returns a detailed shot plan with camera, lighting, and composition specs.

### Step 5: Generate Clips

Delegate to `generate-clips-agent` with the shot plan, references, and character designs.

Pass:
- Shot plan from Step 4
- Reference image paths
- Character design data
- Mode setting

The agent returns paths to generated video clips.

### Step 6: Review Quality

Delegate to `review-quality-agent` with the generated clip paths and original shot plan.

Pass:
- Paths to generated clips
- The original shot plan for comparison

The agent returns a quality assessment with pass/fail, score, and feedback.

**Quality Loop**: If `passed` is `False` and `iteration < max_iterations`, increment iteration and return to Step 5 with the review feedback. If `passed` is `True` or `iteration >= max_iterations`, proceed to Step 7.

### Step 7: Assemble Final

Delegate to `assemble-final-agent` with all approved clip paths.

Pass:
- All clip paths that passed quality review
- Desired transition type

The agent returns the final assembled video path.

## Edge Cases

- **Invalid input**: If the user provides a non-video file or a path that doesn't exist, return an error immediately without running the pipeline.
- **Mode switching**: The mode can be changed between runs. Always use the mode the user specified.
- **Partial results**: If a step fails critically, return partial results with an error message rather than crashing.
- **Quality loop exhaustion**: If max_iterations is reached without passing quality review, proceed with the best available clips and note the quality limitation in the final output.

## Mode-Specific Notes

- **local**: All processing runs on local infrastructure (LM Studio, ComfyUI). Slower but private.
- **cloud**: Uses cloud APIs (Gemini, Veo). Fastest and highest quality but requires API keys.
- **hybrid** (default): Analysis runs locally, video generation runs in cloud. Best balance.