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FacundoDuranDev
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filter-development

Guide adding, modifying, or debugging image filters in Python and C++.

Updated Feb 4, 2026
Search Tags:#debugging#python#pipeline#filters#cpp#image-processing#pybind
One-click install
npx skills add https://github.com/FacundoDuranDev/Spatial-Iteration-Engine --skill filter-development
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: filter-development
Source: https://github.com/FacundoDuranDev/Spatial-Iteration-Engine/tree/main/.claude/skills/filter-development
Command: npx skills add https://github.com/FacundoDuranDev/Spatial-Iteration-Engine --skill filter-development

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Use when adding, modifying, or debugging image filters in Python or C++ within adapters/processors/filters/ or cpp/src/filters/

Core Features & Use Cases

  • Provides a clear blueprint for implementing and testing new filters
  • Demonstrates in-place frame modification patterns and compatibility with the engine

Quick Start

Create a new filter by following the guide and run tests to verify behavior.

Frequently Asked Questions about filter-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add a new image filter in Python and C++ using pybind?▼

You can add an image filter by following the structured blueprint for implementing new processors under adapters/processors/filters/ and cpp/src/filters/. This ensures safe, in-place frame processing and full engine compatibility.

What are the coding patterns for in-place frame modification in image processing pipelines?▼

In-place frame modification requires following specific coding patterns and conventions detailed for the Spatial-Iteration-Engine ecosystem. These patterns ensure safe memory handling when modifying image data directly within the processing pipeline.

How do I debug existing image filters across Python and C++ components?▼

Debugging image filters across Python and C++ involves using the provided references to existing components and documentation. This guides you through the expected coding patterns and testing requirements to isolate and resolve processing issues.

Does modifying cpp/src/filters require specific testing expectations for the pipeline?▼

Yes, modifying cpp/src/filters requires meeting specific testing expectations to verify behavior. Running tests after following the implementation guide ensures your changes integrate safely with the engine's image processing pipeline.

Why should I use structured guidance for image filter development instead of writing standalone scripts?▼

Structured guidance for image filter development ensures your code adheres to the engine's required conventions and integration steps. This prevents compatibility issues and maintains safe, in-place frame processing across the ecosystem.

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