myreels-storyboard

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name: myreels-storyboard description: | Professional storyboard design tool for short drama/video production. Activates when user mentions: script, storyboard, story board, shot design, video production planning, short drama. Workflow: story → character design → storyboard design → CSV/table confirmation → guide user to myreels-api for image/video generation. This skill focuses on storyboard design and content planning; actual image/video generation requires myreels-api skill.

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MyReels Storyboard Design Tool

Integration with myreels-api

myreels-storyboard (storyboard design)
       ↓ generates storyboard CSV
myreels-api (image/video generation) ← user must install this skill first

Important: This skill handles storyboard design. After completion, guide the user to use myreels-api skill for actual image and video generation.

Complete Workflow

1. Story Input → User provides plot outline
2. Story Understanding → Use the active OpenClaw default LLM to understand plot, roles, relationships, and visual beats
3. Character Design → Design characters with a single-image three-view turnaround sheet (front/side/back)
4. Shot Breakdown → Generate structured shots
5. User Confirmation → Output CSV + platform table
6. Image Generation → Guide user to myreels-api
7. Video Creation → Guide user to myreels-api (optional)

Step 1: Collect User Requirements

Collect the following information:

| Question | Options | |----------|---------| | Story content | Plot outline or full script | | Video style | cinematic / anime / 3d cartoon / realistic | | Duration | e.g., 60 seconds, 2 minutes | | Shot count | e.g., 10-15 shots | | Aspect ratio | 16:9 / 9:16 / 1:1 (vertical = 9:16) | | Has dialogue | Yes / No |

After collecting inputs:

Use the current OpenClaw session's default LLM to understand the story semantically

Infer core roles, recurring relationships, and phase-by-phase visual beats from the user's actual plot, not only from surface keywords

Do not ask the user to configure an extra LLM just to use this skill

Do not rely on a bundled local storyboard script for core planning; OpenClaw should draft the CSVs directly

The old helper scripts/generate_storyboard.py is deprecated and should not be referenced in this workflow

Step 2: Character Design (Important!)

Before shot design, establish character consistency using a three-view character sheet (三视图).

Default rule:

Treat character design as a single-image turnaround sheet, not as a poster

The first-pass deliverable should show the same character's front view, side view, and back view on one canvas

Keep outfit, body shape, hairstyle, and accessories identical across all three views

Use full-body neutral standing pose and a plain background unless the user asks otherwise

Recommend nano-banana2 first for generating the base three-view character sheet

Prefer a character-first workflow: approve the character sheet and tags first, then generate storyboard shots with those tags injected

Character Design Fields

| Field | Description | |-------|-------------| | character_name | Character name | | story_role | Role in story, e.g. protagonist / opponent / companion | | relation_to_main | Relationship to the main character, e.g. self / conflict / ally / romance | | appearance | Physical description (hair, eyes, face, build) | | outfit | Clothing and accessories | | personality | Key personality traits | | reference_model | Base model for the first turnaround sheet, default nano-banana2 | | single_image_turnaround_prompt | Main English prompt for one-sheet front/side/back generation | | front_view | English description for front view | | side_view | English description for side view | | back_view | English description for back view | | character_tags | Consistency tags for AI prompts (e.g., "short hair, left face mole, red earrings") | | negative_prompt | Terms to suppress poster-like, inconsistent, or cropped outputs |

Three-View Diagram Output

Generate character descriptions for AI image generation. The output should be structured for a single image containing all three views:

CHARACTER: [Name]
STORY_ROLE: [protagonist / opponent / companion / love_interest]
RELATION_TO_MAIN: [self / conflict / ally / romance]
TAGS: [Consistency tags for prompt injection]
REFERENCE_MODEL: [Usually nano-banana2 for the first three-view sheet]
SINGLE_IMAGE_TURNAROUND_PROMPT: [English prompt explicitly requesting one image with front / side / back full-body views]
NEGATIVE_PROMPT: [English suppression terms for layout drift]
FRONT: A [age] [ethnicity] [gender] with [hair], [eyes], [build], wearing [outfit], neutral standing pose
SIDE: Same character from side profile showing [distinctive feature / silhouette / accessory placement]
BACK: Same character from behind showing [hairstyle back shape / outfit back detail / asymmetry]

Prompt rule:

The single_image_turnaround_prompt must front-load the layout requirement: one image, same character, front view, side view, back view, aligned left to right

Do not output three unrelated portrait prompts as the default character design result

Only add expression sheets, weapons, or alternate outfits after the base three-view sheet is approved

Character Consistency

For each character, build a character tag library that will be reused in all shot prompts:

# Example character tags
"短发女孩, 左脸有痣, 红色耳钉, 黑色皮夹克"
→ "short hair, beauty mark on left cheek, red earrings, black leather jacket"

Character-First Mode

Recommended operating mode:

Generate characters.csv first

Generate relationships.csv for recurring character pairs

Lock the main character's single_image_turnaround_prompt, negative_prompt, and character_tags

Only define extra character rows when the story actually implies them; do not invent an opponent, love_interest, or companion just because the genre suggests one

Reuse the approved character_tags in every visual_prompt

Treat the first approved character row as the anchor for downstream storyboard drafts

In multi-character stories, inject only the characters relevant to each shot phase instead of dumping everyone into every prompt

Build relationships.csv as a pairwise matrix for all recurring character pairs, not only protagonist-centered pairs

Reuse relationship_type and prompt_hint when designing confrontation, ally, or romance shots

Also use visual_relation, emotional_relation, and blocking_pattern to keep staging and mood consistent across repeated pair shots

Let relationship_type bias the default shot language: conflict leans toward low-angle / confrontational framing, ally toward cooperative full shots, romance toward closer eye-level coverage

Let theme bias the baseline shot rhythm too: action leans kinetic, romance leans intimate, thriller leans uneasy, sci-fi leans scale/discovery

In scenes with 3+ recurring characters, choose the active pair/group per shot based on the current beat; do not force every established relationship into every shot

Default OpenClaw behavior:

The skill should use the active OpenClaw default LLM to draft characters.csv, relationships.csv, and storyboard.csv

The CSVs should reflect narrative understanding, not just mechanical keyword expansion

Keep CSV column names stable in English, but make user-facing cell values follow the user's language whenever possible

Keep AI-facing prompt fields in English for cross-model portability: single_image_turnaround_prompt, negative_prompt, front_view_prompt, side_view_prompt, back_view_prompt, character_tags, prompt_hint, and visual_prompt

Keep IDs and machine-oriented codes stable: character_id, relationship_id, shot_id, scene_code

visual_prompt should preserve concrete story specifics from the user's outline in English, including names, props, locations, and beat-specific actions

description and action should vary shot by shot; avoid repetitive generic phase labels when a more specific beat description is available

For mixed-language input, keep description in the dominant user language; if no dominant language is clear, follow the user's most recent instruction language

If a downstream automation flow later needs normalized English enum values, create a secondary English export instead of replacing the user-facing review CSV

Step 3: Generate Storyboard

Shot Structure

Each shot includes these fields:

| Field | Description | |-------|-------------| | shot_id | Scene + Shot number (e.g., S01-01) | | shot_type | Shot size | | camera_angle | Camera angle | | movement | Camera movement | | duration | Estimated seconds (2-5s typical) | | description | User language description - brief summary in user's input language (Chinese/Japanese/English). NOT used for AI generation. | | visual_prompt | English prompt for AI image generation | | action | Character action description | | dialogue | Dialogue/voiceover (optional) | | emotion | Target audience emotion | | sound_fx | Sound effects / music cues (optional) | | notes | Additional notes | | difficulty | 🟢 simple / 🟡 medium / 🔴 complex | | image_url | Generated image URL from downstream tool (optional) | | video_url | Generated video URL from downstream tool (optional) | | status | Workflow status in the user's language for review CSVs; normalize in the English export if needed |

Shot Type Reference

| Type | Chinese | Use Case | |------|---------|----------| | Extreme Close-up / ECU | 大特写 | Key detail, extreme emotion | | Close-up / CU | 特写 | Face, key object | | Medium Close-up / MCU | 中近景 | Dialogue, slight conflict | | Medium / MS | 中景 | Conversation, interaction | | Full Shot / FS | 全景 | Full body, relationship | | Wide / WS | 远景 | Environment, establishing |

Camera Angles & Psychology

| Angle | Emotion Implication | |-------|---------------------| | Eye-level | Neutral, objective | | Low-angle (仰视) | Power, authority, threat | | High-angle (俯视) | Vulnerability, submission | | Dutch | Unease, tension, chaos | | POV | Subjective, immersion | | Tilted | Unstable, danger |

Camera Movement

Fixed / Static

Dolly In (推近) / Dolly Out (拉远)

Pan (摇镜) / Tilt (倾斜)

Follow / Tracking (跟拍)

Orbit (环绕) / Crane (升降)

Handheld (手持) / Breath-like (呼吸感)

Shot Difficulty Grading

| Grade | Icon | Description | |-------|------|-------------| | Simple | 🟢 | Fixed camera, single subject, no complex interaction | | Medium | 🟡 | Light camera movement, dual subject, simple effects | | Complex | 🔴 | Fast motion, multiple subjects, complex choreography, special lighting/particles |

Emotion Mapping

Each shot must specify target emotion:

| Emotion | Key | Description | |---------|-----|-------------| | Hook | 钩子 | Attention grab | | Tension | 紧张 | Building suspense | | Conflict | 冲突 | Confrontation | | Sweet | 甜宠 | Romance, warmth | | Twist | 反转 | Surprise | | Climax | 高潮 | Peak emotional moment | | Release | 释然 | Resolution |

Step 4: AI Prompt Engineering

Prompt Structure

[Subject] + [Action] + [Environment] + [Lighting] + [Quality] + [Style] + [Camera]

Prompt design rule for this skill:

single_image_turnaround_prompt should use layout-first language for nano-banana2

visual_prompt should carry theme + phase cues so action / romance / thriller / sci-fi drafts do not collapse into the same generic shot language

Required Quality Tags

Always include:

cinematic

8k or high resolution

shallow depth of field or bokeh

dynamic lighting

Control Tags

If needed, add:

no text / no watermark / no distorted face

solo / two-shot / crowd

Character Consistency in Prompts

Always inject character tags from Step 2:

# Before
"A woman fighting aliens"
After (with character tags)
"A young woman with short hair, beauty mark on left cheek, red leather jacket fighting aliens, cinematic, 8k..."

Scene Consistency

Use scene code +固化环境描述:

SCENE-A: "abandoned colony base, red dust, two moons, ruins"
All shots in SCENE-A reference this environment

Step 5: Output Confirmation

Multi-Platform Output

Feishu: Use feishu_bitable for online editing Telegram: Formatted text table or CSV attachment Other: Default to CSV

Storyboard Table Fields

shot_id, scene_code, shot_type, camera_angle, movement, duration, description, visual_prompt, action, dialogue, emotion, sound_fx, notes, difficulty, image_url, video_url, status

Note:

The CSV headers stay in English for schema stability

Human-facing values in all three CSVs should follow the user's language

The visual_prompt field is always in English for AI image generation

If automation needs English enums such as shot_type, camera_angle, movement, emotion, difficulty, or status, write a separate English export copy instead of mutating the review CSV

Step 5A: Project Workspace Management

When this skill writes files, it should automatically create a project workspace in the user's current working directory or requested output directory.

Default root pattern:

storyboard-projects/YYYYMMDD-project-slug/

Recommended structure:

storyboard-projects/YYYYMMDD-project-slug/
├── 00-brief/
│   ├── story-brief.md
│   └── user-notes.md
├── 01-characters/
│   ├── drafts/
│   │   ├── characters.v1.draft.csv
│   │   └── character-design.v1.draft.md
│   ├── approved/
│   │   ├── characters.approved.csv
│   │   └── character-design.approved.md
│   ├── exports/
│   │   └── characters.program-en.csv
│   └── generated-images/
│       ├── raw/
│       ├── approved/
│       ├── requests/
│       ├── tasks/
│       └── metadata/
├── 02-relationships/
│   ├── drafts/
│   │   └── relationships.v1.draft.csv
│   ├── approved/
│       └── relationships.approved.csv
│   └── exports/
│       └── relationships.program-en.csv
├── 03-storyboard/
│   ├── drafts/
│   │   ├── storyboard.v1.draft.csv
│   │   └── storyboard-notes.v1.draft.md
│   ├── approved/
│       ├── storyboard.approved.csv
│       └── storyboard-notes.approved.md
│   └── exports/
│       └── storyboard.program-en.csv
├── 04-generation/
│   ├── storyboard-images/
│   │   ├── raw/
│   │   └── approved/
│   │   ├── requests/
│   │   ├── tasks/
│   │   └── metadata/
│   └── videos/
│       ├── raw/
│       ├── approved/
│       ├── requests/
│       ├── tasks/
│       └── metadata/
├── 05-review/
│   └── review-log.md
└── 06-delivery/
    └── manifest.md

Workspace rules:

Do not write user project artifacts back into the skill definition folder itself

Create the project root before saving the first CSV, markdown note, image, or video

Save editable drafts under drafts/

After user confirmation, freeze a copy under the matching approved/ folder instead of overwriting history

Save character three-view turnaround images under 01-characters/generated-images/raw/

Move or copy user-confirmed character reference images into 01-characters/generated-images/approved/

Save storyboard shot images under 04-generation/storyboard-images/raw/

Save storyboard videos under 04-generation/videos/raw/

Move or copy user-confirmed storyboard media into the matching approved/ media folder

For every myreels-api generation, save the exact request payload under requests/

Save task lookup snapshots and task IDs under tasks/

Save response metadata under metadata/, including model name, mode, aspect ratio, seed if available, source image references, created time, and returned URLs

Keep request / task / metadata filenames aligned with the output artifact name so reruns can reuse the same context

Record major review decisions in 05-review/review-log.md

Summarize the final approved artifact set in 06-delivery/manifest.md

Versioning rules:

Use file names like characters.v1.draft.csv, storyboard.v2.draft.csv, images-shot-S01-01.v1.png

Use *.approved.* for frozen user-confirmed files

When revising an approved artifact, create a new draft version instead of mutating the approved snapshot

Put normalized program-facing CSV exports in exports/ with names such as storyboard.program-en.csv and relationships.program-en.csv

Use aligned sidecar records such as character-C001-turnaround.v1.request.json, character-C001-turnaround.v1.task.json, character-C001-turnaround.v1.meta.json

Use the same pattern for storyboard shots and videos, for example shot-S01-01.v2.request.json and shot-S01-01.v2.meta.json

If the user already provided a target directory, follow it and only apply this structure inside that location

Re-run stability rules:

Before re-running a generation, read the latest matching request, task snapshot, and metadata files first

Reuse the last approved prompt, model, aspect ratio, and source asset references unless the user explicitly asks to change them

If the rerun changes any material parameter such as model, prompt, aspect ratio, seed, duration, or source image, write a new versioned request file instead of overwriting the old one

In review-log.md, note why the rerun happened and what changed relative to the previous attempt

Step 6: Guide to myreels-api

Guidance Message (Image Generation)

Storyboard confirmed. Now use myreels-api skill for image generation.
Recommended workflow:
1. Generate one single-image character turnaround sheet first (front/side/back on one canvas)
2. Prefer nano-banana2 for the base three-view character sheet, then generate key storyboard shots using: nano-banana2 / seedream 5.0 / kling v3 image
3. Verify character consistency across all shots
4. Confirm images before video generation
Say: "Use myreels-api to generate storyboard images"

Guidance Message (Video Generation)

Images confirmed. Now use myreels-api skill for video.
Recommended models (image to video):
Kling O3 / Kling V3 (general video)
Seedance 1.5 Pro SE (high quality)
Wan 2.6 i2v / Hailuo-2.3 (quick preview)
Note: For complex shots (🔴), consider longer duration or multiple takes.
Say: "Use myreels-api to convert these images to video"

Step 7: Quality Control Checklist

For each shot, verify:

[ ] Character matches design (outfit, hair, facial features)

[ ] Lighting direction consistent with scene light source

[ ] Motion trajectory physically possible (no clipping, floating)

[ ] Shot type serves narrative rhythm

[ ] Safe zone reserved (vertical: top/bottom 20% no key elements for subtitles)

[ ] Emotion matches intended audience feeling

Output Files

characters.csv - Character design data

relationships.csv - Relationship matrix for recurring characters

storyboard.csv - Complete storyboard

assets/characters-template.csv - Character CSV starter template

assets/relationships-template.csv - Relationship CSV starter template

assets/storyboard-template.csv - Storyboard CSV starter template

Character reference images (via myreels-api)

Storyboard image URLs (via myreels-api)

Project review notes and approved snapshots under the project workspace

Request payloads, task snapshots, and response metadata for reproducible reruns

Notes

visual_prompt must be in English with quality tags

User can input in Chinese / Japanese / English

In the three CSVs, user-facing values should follow the user's language; only AI-facing prompt fields stay in English

If the workflow needs machine-friendly English enums, generate a separate *.program-en.csv export instead of changing the review CSV

Each shot: 2-5 seconds recommended

Always establish character design before shot breakdown

Use difficulty grading to manage AI generation expectations

In OpenClaw, this skill should primarily rely on the active session model for story understanding and CSV drafting

Do not require users to provide extra LLM credentials just to use this skill in OpenClaw

No local storyboard-generation script is required; draft the CSVs natively in OpenClaw

Automatically create a project workspace so drafts, approvals, images, videos, and markdown notes remain organized

Persist generation requests and metadata so image/video reruns can stay stable

See references/ for detailed guides

visual_prompt must be in English with quality tags

User can input in Chinese / Japanese / English

In the three CSVs, user-facing values should follow the user's language; only AI-facing prompt fields stay in English

If the workflow needs machine-friendly English enums, generate a separate *.program-en.csv export instead of changing the review CSV

Each shot: 2-5 seconds recommended

Always establish character design before shot breakdown

Use difficulty grading to manage AI generation expectations

In OpenClaw, this skill should primarily rely on the active session model for story understanding and CSV drafting

Do not require users to provide extra LLM credentials just to use this skill in OpenClaw

No local storyboard-generation script is required; draft the CSVs natively in OpenClaw

Automatically create a project workspace so drafts, approvals, images, videos, and markdown notes remain organized

Persist generation requests and metadata so image/video reruns can stay stable

See references/ for detailed guides