myreels-storyboard

Professional storyboard design tool for short drama/video production. Activates when user mentions: script, storyboard, story board, shot design, video produ...

Security Scan

The skill is an instruction-only storyboard/design helper whose requested footprint (no installs, no env vars, no binaries) is consistent with its stated purpose.

Purpose & Capability

Name/description (storyboard design) aligns with the included assets, CSV templates, and prompts. It does not request unrelated credentials or binaries and only recommends using a separate myreels-api skill for image/video generation.

Instruction Scope

SKILL.md stays focused on collecting story inputs, producing character/relationship/storyboard CSVs, and creating generation-ready English prompts. It does not instruct reading system files, environment secrets, or contacting unknown endpoints itself. It does reference a myreels-api endpoint for model parameters but only as a downstream integration point.

Install Mechanism

No install spec and no code files — instruction-only. Nothing is downloaded or written by an installer in the skill bundle.

Credentials

The skill declares no required environment variables, credentials, or config paths. Its recommended workflow may require the separate myreels-api skill (which could request credentials), but that is external and documented as a separate dependency.

Persistence & Privilege

Flags show normal privileges (always:false, model invocation allowed). The skill does not request persistent system presence or modifications to other skills or global agent settings.

Assessment

This skill is instruction-only and internally consistent for storyboard and character-sheet generation. Before installing: note that it delegates actual image/video generation to a separate myreels-api skill (that other skill may require API keys or network access), and the CSVs/prompts it produces will contain user-provided story details (which may be sensitive). If you plan to generate images/videos, review the myreels-api skill's permissions and credentials. Otherwise, the storyboard skill itself does not ask for secrets or perform external network access.

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