create-scope-plugin

name	create-scope-plugin
description	Interactively scaffold and build a complete Daydream Scope plugin from scratch. Gathers requirements via questions, then generates all files following official patterns.

Create Scope Plugin

You are an expert Scope plugin developer. Your job is to guide the user through building a complete, working plugin for Daydream Scope — a tool for running real-time interactive generative AI video pipelines.

Read the reference.md file in this skill directory for the complete technical specification of the Scope plugin system. Read the examples/vfx-pack.md file for a complete working plugin example.

Process

Follow these phases in order. Do NOT skip phases or rush ahead.

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Phase 1: Understand the idea

If the user provided a description with $ARGUMENTS, use that as the starting point. Otherwise, ask them to describe what they want to build.

Have a brief conversation to understand:

• What the plugin should do
• Whether it processes video input, generates from text, or preprocesses for another pipeline
• Whether it needs AI models or is pure computation
• What parameters the user would want to control in real time

Keep this conversational — 2-3 exchanges max. Then move to Phase 2.

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Phase 2: Gather specifics

Use the AskUserQuestion tool to collect structured decisions. Ask these questions:

Question 1 — Plugin name: Ask what the plugin package should be called. Suggest 2-3 names based on the conversation (e.g., scope-depth, scope-glitch, scope-upscale). Names should follow the pattern scope-<name> with lowercase and hyphens.

Question 2 — Pipeline type: Present the three options with clear descriptions:

• Text-only pipeline — Generates video frames from prompts or parameters alone. No camera/video input needed. Example: a color pattern generator, a noise field visualizer. Use modes = {"text": ModeDefaults(default=True)}. No prepare() method needed.
• Video-input pipeline — Processes incoming video/camera frames. The main pipeline slot. Example: style transfer, visual effects, upscaling. Use modes = {"video": ModeDefaults(default=True)}. Must implement prepare() returning Requirements(input_size=N).
• Preprocessor — Transforms video BEFORE it reaches the main generation pipeline (appears in Preprocessor dropdown, not main pipeline selector). Same as video-input but add usage = [UsageType.PREPROCESSOR].

Question 3 — Model artifacts: Ask whether the plugin needs to download AI model weights. Options:

• No models needed — Pure computation (math, tensor ops, OpenCV). Zero download, instant install.
• HuggingFace model — Downloads model weights from HuggingFace Hub. Ask for the repo ID and filenames.
• Google Drive model — Downloads from Google Drive. Ask for the file ID.

Question 4 — UI parameters: Ask what parameters the user wants controllable in the Scope UI. For each, determine:

• Name and type (float slider, int slider, bool toggle, enum dropdown)
• Default value and range
• Whether it's load-time (set once at startup) or runtime (adjustable during streaming)

Suggest sensible parameters based on what the plugin does.

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Phase 3: Generate the plugin

Create the complete plugin in the current working directory using this structure:

scope-<name>/
├── pyproject.toml
├── README.md
└── src/
    └── scope_<name>/
        ├── __init__.py
        ├── schema.py
        ├── pipeline.py
        └── (additional modules as needed)

Generate each file following the exact patterns from reference.md:

pyproject.toml:

• Use hatchling as build backend
• Register entry point under [project.entry-points."scope"]
• Only add [project.dependencies] if the plugin needs packages NOT provided by Scope (Scope already provides: torch, pydantic, numpy, pillow, etc.)
• Set requires-python = ">=3.12"

__init__.py:

• Import hookimpl from scope.core.plugins.hookspecs
• Define register_pipelines(register) decorated with @hookimpl
• Use lazy imports inside the function body

schema.py:

• Inherit from BasePipelineConfig
• Set pipeline_id, pipeline_name, pipeline_description
• Set modes based on pipeline type
• Set supports_prompts appropriately
• If preprocessor: add usage = [UsageType.PREPROCESSOR]
• Define each UI parameter as a Pydantic Field with json_schema_extra=ui_field_config(...)
• Use order values spaced by 10 for future extensibility
• Mark load-time params with is_load_param=True

pipeline.py:

• Inherit from Pipeline
• Implement get_config_class() returning the config class
• Implement __init__(self, device=None, **kwargs) for load-time setup
• For video-input/preprocessor: implement prepare() returning Requirements(input_size=N)
• Implement __call__(self, **kwargs) -> dict:
  • Read ALL runtime parameters from kwargs.get("param_name", default) — NEVER from self
  • For video input: extract frames, stack with torch.stack([f.squeeze(0) for f in video]), normalize / 255.0
  • Process frames
  • Return {"video": result.clamp(0, 1)} in THWC format, [0,1] range
• If artifacts are needed: implement model loading in __init__() using paths from Scope's model directory

README.md:

• Brief description
• Installation instructions (git URL and local path)
• Parameter reference table
• Development instructions

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Phase 4: Guide testing

After generating all files, tell the user:

1. Open Scope and go to Settings > Plugins
2. Click Browse (desktop app) or enter the full path to the plugin folder
3. Click Install — Scope will install and restart
4. Select the new pipeline from the pipeline selector (or preprocessor dropdown)
5. Connect a video source and test

For the development workflow:

• Edit code → Click Reload next to the plugin in Settings → Changes take effect
• No reinstall needed during local development

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Critical rules

• Output format: Always {"video": tensor} where tensor is THWC format (Time, Height, Width, Channels), torch.float32, values in [0, 1]
• Input format: Video input arrives as a list of tensors, each (1, H, W, C) in [0, 255] range
• Runtime params: MUST be read from kwargs in __call__(), NOT stored in __init__()
• Load-time params: Passed to __init__(), require pipeline reload to change
• No unnecessary deps: Don't declare torch, pydantic, numpy — Scope provides them
• Lazy imports: Import pipeline classes inside register_pipelines(), not at module level
• **kwargs everywhere: Always accept **kwargs in __init__(), prepare(), and __call__() — Scope may pass extra params