ai-video-producer

name: ai-video-producer description: > Complete AI video production workflow using WAN 2.1 and Qwen Image Edit 2511 models via ComfyUI. Creates any video type: promotional, educational, narrative, social media, animations, game trailers, music videos, product demos, and more. Use when users want to create videos with AI, need help with video storyboarding, keyframe generation, or video prompt writing. Follows a philosophy-first approach: establish visual style and production philosophy, then execute scene by scene with user feedback at each stage. Supports advanced features like layer-based compositing, reference-based generation, and style consistency. Runs locally on RTX 3080+ (10GB+ VRAM). allowed-tools: Bash, Read, Write, Edit, Glob, AskUserQuestion, TodoWrite

AI Video Producer

Create professional AI-generated videos through a structured, iterative workflow using local models.

Prerequisites & Auto-Setup

Requires: WAN 2.1 + Qwen Image Edit via ComfyUI (GGUF quantization, ~40GB models)

Setup Commands

python {baseDir}/scripts/setup_comfyui.py         # Full setup (first time)
python {baseDir}/scripts/setup_comfyui.py --check # Verify setup
python {baseDir}/scripts/setup_comfyui.py --start # Start server before generating

System Requirements

See README.md for detailed setup instructions.

MANDATORY WORKFLOW REQUIREMENTS

YOU MUST FOLLOW THESE RULES:

1. ALWAYS use TodoWrite at the start to create a task list for the entire workflow
2. ALWAYS ask about Approval Mode at the very start (see below)
3. NEVER skip phases - complete each phase in order before proceeding
4. ALWAYS create required files - philosophy.md, style.json, scene-breakdown.md, and pipeline.json are REQUIRED
5. ALWAYS break videos into multiple scenes - minimum 2 scenes for any video over 5 seconds
6. NEVER generate without a complete pipeline.json - plan ALL prompts first, execute second
7. ALWAYS use execute_pipeline.py for generation - deterministic execution, no ad-hoc commands
8. ALWAYS review generated outputs using VLM - view images after each stage, assess quality

Approval Mode Selection (FIRST STEP)

At the very beginning of the workflow, ask the user to choose an approval mode:

Use AskUserQuestion with these options:

• "Manual approval" - User approves each phase before proceeding (philosophy, scenes, pipeline, assets, keyframes, videos)
• "Automatic approval" - LLM proceeds automatically, user only reviews final output

Store the selected mode and apply it to all checkpoints throughout the workflow.

Pipeline-Based Architecture

This skill uses a two-phase approach:

Phase A: Planning (LLM-Driven)

• LLM creates philosophy.md, style.json, scene-breakdown.md
• LLM generates ALL prompts and stores in pipeline.json
• User reviews and approves the complete plan before any generation

Phase B: Execution (Programmatic)

• execute_pipeline.py reads pipeline.json and executes deterministically
• LLM reviews outputs using VLM capability after each stage
• User approves or requests regeneration

Benefits:

• All prompts visible before ANY generation starts
• Deterministic execution - no LLM deviation during generation
• Reproducible - same pipeline.json = same commands executed
• Traceable - status tracking in pipeline.json

Pipeline Mode: Scene/Segment v3.0

Proper hierarchical structure distinguishing Scenes (narrative/cinematographic units) from Segments (5-second technical chunks).

Key Concepts:

• Scene: A continuous shot from a single camera perspective (e.g., "woman at cafe table", "phone screen close-up")
• Segment: A 5-second video chunk within a scene (due to model limitations)
• Transition: How scenes connect ("cut", "continuous", "fade", "dissolve")

Scene 1 (cut)           Scene 2 (continuous)       Scene 3 (fade)
┌─────────────────┐     ┌─────────────────┐       ┌─────────────────┐
│ KF (generated)  │     │ KF (extracted)  │       │ KF (generated)  │
│ ┌─────────────┐ │     │ ┌─────────────┐ │       │ ┌─────────────┐ │
│ │ Segment A   │ │     │ │ Segment A   │ │       │ │ Segment A   │ │
│ └─────────────┘ │     │ └─────────────┘ │       │ └─────────────┘ │
│ ┌─────────────┐ │     └─────────────────┘       └─────────────────┘
│ │ Segment B   │ │             │                         │
│ └─────────────┘ │             │ continuous              │ fade
└─────────────────┘             ▼                         ▼
        │ cut          ─────────────────          ─────────────────
        ▼              Final merged video with transitions

Pros:

• Semantic clarity (scenes = narrative units, segments = technical units)
• Scene-level keyframes (generated for cuts, extracted for continuous)
• Automatic video merging with transitions (cut, fade, dissolve)
• Hierarchical status tracking

Cons: More complex schema

When to use each transition:

CRITICAL: Character Scenes and Continuous Transitions

When a scene contains a character (even partially visible, like hands or clothing), do NOT use "extracted" keyframes. Extracted keyframes lose character identity anchoring and cause visual drift (e.g., clothing color changes, style inconsistency).

Rule: If ANY part of a character is visible (hands, clothing, body), use "type": "generated" with character references.

Standard Checkpoint Format (ALL PHASES)

Checkpoint behavior depends on the selected Approval Mode:

Manual Approval Mode (default)

1. Show the output to user (file path or display content)
2. Ask for approval using AskUserQuestion:
   • "Approve" - Proceed to next step
   • User can select "Other" to specify what needs to be changed
3. If user does not approve:
   • User specifies what to change
   • Make the requested adjustments
   • Show updated result → Ask again → Repeat until approved
4. Do NOT proceed to next phase until approved

Automatic Approval Mode

1. Show the output to user (file path or display content)
2. LLM reviews the output using VLM capability (for images/videos)
3. If LLM assessment is positive → Proceed automatically
4. If LLM detects issues → Fix and regenerate before proceeding
5. User only reviews final output at the end

Workflow Phases (MUST COMPLETE IN ORDER)

Phase 1: Production Philosophy (REQUIRED)

DO NOT PROCEED TO PHASE 2 UNTIL BOTH FILES EXIST:

• {output_dir}/philosophy.md
• {output_dir}/style.json

Step 1.1: Create philosophy.md

Create this file with ALL sections filled in:

# Production Philosophy: [Project Name]

## Visual Identity
- **Art Style**: [e.g., cinematic realistic, stylized animation, painterly]
- **Color Palette**: [primary colors, mood, temperature]
- **Lighting**: [natural, dramatic, soft, high-contrast]
- **Composition**: [rule of thirds, centered, dynamic angles]

## Motion Language
- **Movement Quality**: [smooth/fluid, dynamic/energetic, subtle/minimal]
- **Pacing**: [fast cuts, slow contemplative, rhythmic]
- **Camera Style**: [static, tracking, handheld, cinematic sweeps]

## Subject Consistency
- **Characters/Products**: [detailed descriptions for consistency]
- **Environment**: [setting details that persist across scenes]
- **Props/Elements**: [recurring visual elements]

## Constraints
- **Avoid**: [unwanted elements, styles, or actions]
- **Maintain**: [elements that must stay consistent]

Step 1.2: Create style.json

Create this file for programmatic use with generation scripts:

{
  "project_name": "Project Name Here",
  "visual_style": {
    "art_style": "description",
    "color_palette": "description",
    "lighting": "description",
    "composition": "description"
  },
  "motion_language": {
    "movement_quality": "description",
    "pacing": "description",
    "camera_style": "description"
  },
  "subject_consistency": {
    "main_subject": "detailed description",
    "environment": "detailed description"
  },
  "constraints": {
    "avoid": ["list", "of", "things"],
    "maintain": ["list", "of", "things"]
  }
}

Step 1.3: CHECKPOINT - Get User Approval

1. Inform user that philosophy.md and style.json have been created
2. Use AskUserQuestion:
   • "Approve" - Proceed to scene breakdown
   • User selects "Other" to specify changes

If user requests changes → make adjustments → ask again → repeat until approved

Phase 2: Scene Breakdown (REQUIRED)

DO NOT PROCEED TO PHASE 3 UNTIL scene-breakdown.md EXISTS AND USER APPROVES

Step 2.1: Analyze Video Requirements

Before creating scenes, determine:

• Total video duration needed
• Number of scenes required (minimum 2 for videos > 5 seconds)
• Key story beats or content moments
• Transitions between scenes

Step 2.2: Create scene-breakdown.md

MANDATORY FORMAT - v3.0 Scene/Segment structure:

# Scene Breakdown: [Project Name]

## Overview
- **Total Duration**: [X seconds]
- **Number of Scenes**: [N]
- **Video Type**: [promotional/narrative/educational/etc.]
- **Pipeline Mode**: Scene/Segment v3.0

---

## Scene Overview

| Scene | Description | Camera | Transition | Duration |
|-------|-------------|--------|------------|----------|
| 1 | [Brief description] | [Camera type] | - | 5s |
| 2 | [Brief description] | [Camera type] | cut/continuous | 5s |

---

## Scenes

### Scene N: [Title]

**Type**: character | landscape
**Duration**: 5 seconds
**Camera**: [static/tracking/pan/zoom]
**Purpose**: [What this scene communicates]

**First Keyframe**: Generated (character scenes) OR Extracted (continuous landscape only)
- Characters: [list IDs - REQUIRED if visible]
- Background: [background ID]

**Segments**:
| ID | Motion | Duration |
|----|--------|----------|
| seg-Na | [Motion description] | 5s |

**Transition to Next**: cut | continuous | fade

[Repeat for all scenes]

Scene Type Explanation:

• character: Scenes with characters - MUST include character references in keyframe
• landscape: Scenes without characters - can use extracted keyframes for continuous transitions

Scene Count Guidelines

WAN generates 5-second clips (81 frames at 16fps)

Step 2.3: CHECKPOINT - Get User Approval

1. Inform user that scene-breakdown.md has been created with [N] scenes
2. Use AskUserQuestion:
   • "Approve" - Proceed to asset generation
   • User selects "Other" to specify changes

If user requests changes → make adjustments → ask again → repeat until approved

Phase 2.5: Asset Generation (REQUIRED)

DO NOT PROCEED TO PHASE 3 UNTIL assets.json EXISTS AND USER APPROVES

This phase creates reusable assets that maintain consistency across all scenes.

Step 2.5.1: Analyze Required Assets

Review scene-breakdown.md and identify all unique:

• Characters (each character that appears in scenes)
• Backgrounds (each unique location/environment)
• Styles (the visual style to apply consistently)
• Objects (any recurring props or items)

Step 2.5.2: Create assets.json

MANDATORY FORMAT:

{
  "characters": {
    "samurai": {
      "description": "Feudal Japanese warrior, red armor, stern expression, dark hair",
      "identity_ref": "assets/characters/samurai.png"
    }
  },
  "backgrounds": {
    "temple_courtyard": {
      "description": "Ancient temple with cherry blossoms, stone paths, morning light",
      "ref_image": "assets/backgrounds/temple_courtyard.png"
    }
  },
  "styles": {
    "ghibli": {
      "description": "Studio Ghibli anime aesthetic, soft colors, painterly",
      "ref_image": "assets/styles/ghibli.png"
    }
  },
  "objects": {
    "katana": {
      "description": "Traditional Japanese sword with black sheath",
      "ref_image": "assets/objects/katana.png"
    }
  }
}

Step 2.5.3: Asset Generation

Assets are generated via execute_pipeline.py --stage assets. This phase defines assets in assets.json for later execution.

Step 2.5.4: CHECKPOINT - Get User Approval

1. Inform user that assets.json and asset images have been created
2. Show the generated assets to user
3. Use AskUserQuestion:
   • "Approve" - Proceed to keyframe generation
   • User selects "Other" to specify which assets need adjustment

If user requests changes → regenerate specific assets → ask again → repeat until approved

Phase 3: Pipeline Generation (REQUIRED)

DO NOT PROCEED TO EXECUTION UNTIL pipeline.json EXISTS AND USER APPROVES

This phase consolidates all prompts into a single structured file that will be executed deterministically.

Step 3.1: Create pipeline.json

Based on philosophy.md, style.json, scene-breakdown.md, and assets.json, create a complete pipeline.json.

Pipeline Schema v3.0

{
  "version": "3.0",
  "project_name": "project-name",
  "metadata": {
    "created_at": "ISO timestamp",
    "philosophy_file": "philosophy.md",
    "style_file": "style.json",
    "scene_breakdown_file": "scene-breakdown.md"
  },
  "assets": {
    "characters": {
      "protagonist": {
        "prompt": "Character sheet description...",
        "output": "assets/characters/protagonist.png",
        "status": "pending"
      }
    },
    "backgrounds": {
      "location": {
        "prompt": "Background description...",
        "output": "assets/backgrounds/location.png",
        "status": "pending"
      }
    }
  },
  "scenes": [
    {
      "id": "scene-01",
      "description": "Scene description",
      "camera": "medium shot",
      "transition_from_previous": null,
      "first_keyframe": {
        "type": "generated",
        "prompt": "Keyframe description...",
        "background": "location",
        "characters": ["protagonist"],
        "output": "keyframes/scene-01-start.png",
        "status": "pending"
      },
      "segments": [
        {
          "id": "seg-01-a",
          "motion_prompt": "Motion description...",
          "output_video": "scene-01/seg-a.mp4",
          "output_keyframe": "keyframes/scene-01-seg-a-end.png",
          "status": "pending"
        }
      ],
      "output_video": "scene-01/merged.mp4",
      "status": "pending"
    }
  ],
  "final_video": {
    "output": "final/video.mp4",
    "status": "pending"
  }
}

Additional scene examples:

• Cut transition: "transition_from_previous": {"type": "cut"}
• Continuous (landscape only): "transition_from_previous": {"type": "continuous"}, "first_keyframe": {"type": "extracted"}

v3.0 Schema Key Concepts:

Keyframe Type Selection (CRITICAL for consistency):

Transition Types:

• cut: Hard cut (instant switch between scenes)
• continuous: Seamless continuation - use "extracted" ONLY for landscape scenes, use "generated" with character refs for character scenes
• fade: Fade through black (duration configurable, default 0.5s)
• dissolve: Cross-dissolve (duration configurable, default 0.5s)

Step 3.2: Pipeline Prompt Writing Guidelines

For Character Prompts:

• Include full physical description (hair, eyes, clothing, distinguishing features)
• Mention "anime style character sheet, A-pose, full body, white background"
• Include multiple views: "front view, side view, back view"

For Background Prompts:

• Describe setting, lighting, atmosphere
• Include "no people, establishing shot"
• Match style from philosophy.md

For Keyframe Prompts:

• Use positional language: "On the left:", "On the right:", "In the center:"
• Reference character appearance from assets
• Include background context and lighting
• Match style from philosophy.md

For Video Prompts (I2V Motion Prompts):

I2V models tend to produce static video. Use these rules:

1. Separate subject motion from camera motion - describe both explicitly
2. Describe physical body movements - "legs pumping", "arms swinging", not just "running"
3. Include environmental interaction - "boots splashing through mud", "hair flowing in wind"
4. Avoid POV/first-person - I2V struggles with perspective-based motion
5. Use motion verbs - "sprinting" not "in motion"

Motion Prompt Structure:

[SUBJECT] [ACTION VERB] [BODY PART DETAILS], [ENVIRONMENTAL INTERACTION], camera [CAMERA MOVEMENT]

Example: "soldier sprints through trench, legs driving forward, rifle bouncing against chest, mud splashing from boots, camera tracking from behind at shoulder height"

Camera Terms: "tracking from behind", "dollying alongside", "pushing in slowly", "holding steady", "panning left/right", "tilting up/down"

See references/prompt-engineering.md for detailed guidance.

Step 3.3: CHECKPOINT - Get User Approval

1. Show the complete pipeline.json to user
2. Highlight all prompts for review
3. Use AskUserQuestion:
   • "Approve" - Proceed to execution
   • User selects "Other" to specify which prompts need adjustment

If user requests changes → update pipeline.json → ask again → repeat until approved

Phase 4: Asset Execution

Execute asset generation using the pipeline executor.

Step 4.1: Run Asset Stage

python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --stage assets

This will:

• Generate all characters, backgrounds defined in pipeline.json
• Automatically use --free-memory for each generation
• Update status in pipeline.json as items complete

Step 4.2: Review Assets with VLM

After execution completes, use the Read tool to view each generated asset:

1. View each character asset - verify appearance matches description
2. View each background asset - verify setting and style

Step 4.3: CHECKPOINT - Get User Approval

1. Show generated assets to user (use Read tool to display images)
2. Report on quality and any issues noticed
3. Use AskUserQuestion:
   • "Approve" - Proceed to keyframes
   • User selects "Other" to specify which assets need regeneration

If regeneration needed:

1. Update the prompt in pipeline.json
2. Run: python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --regenerate <asset_id>
3. Review again → repeat until approved

Phase 5: Keyframe Execution

Execute keyframe generation using the pipeline executor.

Step 5.1: Run Keyframe Stage

python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --stage keyframes

This will:

• Generate all keyframes defined in pipeline.json
• Reference assets by ID (resolved to file paths automatically)
• Use --free-memory for EVERY keyframe (mandatory)
• Update status in pipeline.json as items complete

Step 5.2: Review Keyframes with VLM

After execution completes, use the Read tool to view each keyframe:

1. View each keyframe image
2. Check character consistency with assets
3. Check background consistency
4. Check style matches philosophy

Step 5.3: CHECKPOINT - Get User Approval

1. Show generated keyframes to user (use Read tool to display images)
2. Report on quality and consistency
3. Use AskUserQuestion:
   • "Approve" - Proceed to videos
   • User selects "Other" to specify which keyframes need regeneration

If regeneration needed:

1. Update the prompt in pipeline.json
2. Run: python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --regenerate <KF-id>
3. Review again → repeat until approved

Phase 6: Video Execution

Execute video generation using the pipeline executor.

Step 6.1: Run Video Stage

python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --stage videos

This will:

• Generate all videos defined in pipeline.json
• Reference keyframes by ID (resolved to file paths automatically)
• Use --free-memory only on first video (switching from image to video models)
• Update status in pipeline.json as items complete

Step 6.2: CHECKPOINT - Get User Approval

1. Inform user of generated video locations
2. Use AskUserQuestion:
   • "Approve" - Complete
   • User selects "Other" to specify which videos need regeneration

If regeneration needed:

1. Update the prompt in pipeline.json
2. Run: python {baseDir}/scripts/execute_pipeline.py {output_dir}/pipeline.json --regenerate <scene-id>
3. Review again → repeat until approved

Phase 7: Review & Iterate

Handle any final adjustments requested by user.

Reference: Keyframe Generation Details

The following sections provide reference information used by the pipeline executor.

v3.0 Keyframe Strategy

In Scene/Segment v3.0 mode:

• Each scene has ONE first keyframe (generated or extracted)
• Video generation uses I2V: first keyframe + motion prompt → video
• End frames are automatically extracted from generated videos

Scene 1 (cut)         Scene 2 (cut)         Scene 3 (continuous)
┌─────────────┐       ┌─────────────┐       ┌─────────────┐
│ KF generated│       │ KF generated│       │ KF extracted│
│     ↓       │       │     ↓       │       │     ↓       │
│ I2V video   │ ──→   │ I2V video   │ ──→   │ I2V video   │
│     ↓       │       │     ↓       │       │     ↓       │
│ End extract │       │ End extract │       │ End extract │
└─────────────┘       └─────────────┘       └─────────────┘

Scene Type and Keyframe Rules

Reference Chain Rules

These rules ensure consistency across scenes:

Character Consistency Rules (CRITICAL)

Problem: Without character references, I2V models cause "identity drift" - clothing colors change, styles shift, and characters become unrecognizable across scenes.

Solution: Include character references for ANY scene where the character is visible, even partially.

When to include character references:

Common Mistakes:

• Close-up of hands without character reference → clothing inconsistency
• Using "type": "extracted" for character scenes → identity drift

The Character Drift Problem: Without character references, each scene accumulates deviations. By Scene 4, character may have different clothing color/style.

Solution: Always use "type": "generated" with "characters": ["id"] for any scene with visible character parts.

Reference Slot Allocation

The keyframe generator uses 3 reference image slots:

Note: With --background, maximum 2 characters are supported (3 slots total).

Character Count Decision Matrix

The system has 3 reference image slots. Use this matrix to determine the approach:

Handling 3+ Characters with Background OR 4+ Characters:

When exceeding 3 reference slots:

1. Select 2-3 most important characters for reference slots
2. Describe ALL characters in prompt with positional language
3. Trade-off: Referenced characters have strong identity; others rely on prompt

Keyframe Quality Checklist

Before proceeding to video, verify EACH keyframe:

• Subject appears correctly (no distortion)
• Style matches Production Philosophy
• Composition allows for intended motion
• Characters are consistent with assets
• Background/environment is consistent
• Lighting direction is consistent

Key Rule: NEVER chain keyframes as references - always use original assets from assets/characters/

Output Directory Structure (REQUIRED)

{output_dir}/
├── philosophy.md              # Production philosophy
├── style.json                 # Style configuration
├── scene-breakdown.md         # Scene breakdown with segments
├── pipeline.json              # v3.0 pipeline definition
│
├── assets/                    # Reusable character/background assets
│   ├── characters/
│   │   ├── protagonist.png
│   │   └── sidekick.png
│   └── backgrounds/
│       ├── city_street.png
│       └── rooftop.png
│
├── keyframes/                 # Scene start keyframes + extracted end frames
│   ├── scene-01-start.png    # Generated from assets
│   ├── scene-01-seg-a-end.png # Extracted from video
│   ├── scene-02-start.png    # Generated (cut) or extracted (continuous)
│   └── scene-02-seg-a-end.png
│
├── scene-01/
│   ├── seg-a.mp4             # Segment video
│   └── merged.mp4            # Scene merged video
│
├── scene-02/
│   ├── seg-a.mp4
│   └── merged.mp4
│
└── final/
    └── video.mp4             # All scenes merged

Note: Each scene has one start keyframe. End frames are extracted from generated videos, not pre-generated.

TodoWrite Template

At the START of the workflow, create the appropriate todo list based on selected approval mode:

Manual Approval Mode

1. Ask user to select approval mode (Manual/Automatic)
2. Check ComfyUI setup and start server
3. Create philosophy.md and style.json
4. Get user approval on production philosophy
5. Create scene-breakdown.md
6. Get user approval on scene breakdown
7. Create pipeline.json (v3.0 schema)
8. Get user approval on pipeline.json
9. Execute assets stage, review with VLM, get user approval
10. Execute keyframes stage, review with VLM, get user approval
11. Execute scenes stage, get user approval
12. Provide final summary

Automatic Approval Mode

1. Ask user to select approval mode (Manual/Automatic)
2. Check ComfyUI setup and start server
3. Create philosophy.md and style.json
4. Create scene-breakdown.md
5. Create pipeline.json (v3.0 schema)
6. Execute assets stage, review with VLM
7. Execute keyframes stage, review with VLM
8. Execute scenes stage
9. Present final output to user for review

Key points:

• ALL prompts are written to pipeline.json BEFORE any generation starts
• execute_pipeline.py handles VRAM management automatically
• LLM always reviews outputs with VLM (both modes)
• In Auto mode, LLM proceeds unless VLM detects issues

Setup Verification (Step 1)

python {baseDir}/scripts/setup_comfyui.py --check   # Verify setup
python {baseDir}/scripts/setup_comfyui.py --start   # Start ComfyUI server

VRAM Management

Note: execute_pipeline.py handles VRAM management automatically with --free-memory flags. The Qwen image model and WAN video model cannot both fit in 10GB VRAM simultaneously - the executor handles this.

Quick Reference

Pipeline Execution

Key Features:

• Scene keyframes generated for "cut" transitions, extracted for "continuous" (landscape only)
• Segments within scenes merged automatically
• Final video assembled with transitions (cut/fade/dissolve)

Asset Generation

Keyframe Generation

IMPORTANT: Always use --free-memory for every keyframe generation to prevent VRAM fragmentation.

Camera Angle Transformation

Transform keyframes using angle_transformer.py:

• --rotate: -180 to 180 (horizontal rotation, negative = left)
• --tilt: -90 to 90 (vertical tilt, negative = look up)
• --zoom: wide/normal/close
• Requires Multi-Angle LoRA

Video Generation (I2V Mode)

This skill uses Image-to-Video (I2V) mode exclusively:

• Input: Start keyframe + Motion prompt
• Output: Video + Extracted end frame

Video Model Selection

Motion Limitations: I2V preserves the subject's pose from input image. For dynamic poses, keyframe must show subject mid-action. Camera motion works better than body motion.

Technical Specs

References

• references/models.md - Model specifications and sizes
• references/prompt-engineering.md - Detailed prompt writing guidance
• references/troubleshooting.md - Common issues and solutions
• README.md - Installation instructions