Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "computer-vision-processor",
+  "description": "Computer vision image processing and analysis",
+  "version": "1.0.0",
+  "author": {
+    "name": "Claude Code Plugins",
+    "email": "[email protected]"
+  },
+  "skills": [
+    "./skills"
+  ],
+  "commands": [
+    "./commands"
+  ]
+}

README.md

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+# computer-vision-processor
+
+Computer vision image processing and analysis

commands/process-vision.md

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+---
+description: Execute AI/ML task with intelligent automation
+---
+
+# AI/ML Task Executor
+
+You are an AI/ML specialist. When this command is invoked:
+
+1. Analyze the current context and requirements
+2. Generate appropriate code for the ML task
+3. Include data validation and error handling
+4. Provide performance metrics and insights
+5. Save artifacts and generate documentation
+
+Support modern ML frameworks and best practices.

plugin.lock.json

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+{
+  "$schema": "internal://schemas/plugin.lock.v1.json",
+  "pluginId": "gh:jeremylongshore/claude-code-plugins-plus:plugins/ai-ml/computer-vision-processor",
+  "normalized": {
+    "repo": null,
+    "ref": "refs/tags/v20251128.0",
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+    "generatedAt": "2025-11-28T10:18:13.883523Z",
+    "toolVersion": "publish_plugins.py@0.2.0"
+  },
+  "origin": {
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+    "branch": "master",
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+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
+  },
+  "manifest": {
+    "name": "computer-vision-processor",
+    "description": "Computer vision image processing and analysis",
+    "version": "1.0.0"
+  },
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+      },
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+    "flags": []
+  }
+}

skills/computer-vision-processor/SKILL.md

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+---
+name: processing-computer-vision-tasks
+description: |
+  This skill enables Claude to process and analyze images using computer vision techniques. It's used to perform tasks such as object detection, image classification, and image segmentation. Use this skill when a user requests analysis of an image, asks for identification of objects within an image, or needs help with other computer vision related tasks. Trigger terms include "analyze image", "object detection", "image classification", "image segmentation", "computer vision", "process image", or when the user provides an image and asks for insights.
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill empowers Claude to leverage the computer-vision-processor plugin to analyze images, detect objects, and extract meaningful information. It automates computer vision workflows, optimizes performance, and provides detailed insights based on image content.
+
+## How It Works
+
+1. **Analyzing the Request**: Claude identifies the need for computer vision processing based on the user's request and trigger terms.
+2. **Generating Code**: Claude generates the appropriate Python code to interact with the computer-vision-processor plugin, specifying the desired analysis type (e.g., object detection, image classification).
+3. **Executing the Task**: The generated code is executed using the `/process-vision` command, which processes the image and returns the results.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Analyze an image for specific objects or features.
+- Classify an image into predefined categories.
+- Segment an image to identify different regions or objects.
+
+## Examples
+
+### Example 1: Object Detection
+
+User request: "Analyze this image and identify all the cars and pedestrians."
+
+The skill will:
+1. Generate code to perform object detection on the provided image using the computer-vision-processor plugin.
+2. Return a list of bounding boxes and labels for each detected car and pedestrian.
+
+### Example 2: Image Classification
+
+User request: "Classify this image. Is it a cat or a dog?"
+
+The skill will:
+1. Generate code to perform image classification on the provided image using the computer-vision-processor plugin.
+2. Return the classification result (e.g., "cat" or "dog") along with a confidence score.
+
+## Best Practices
+
+- **Data Validation**: Always validate the input image to ensure it's in a supported format and resolution.
+- **Error Handling**: Implement robust error handling to gracefully manage potential issues during image processing.
+- **Performance Optimization**: Choose the appropriate computer vision techniques and parameters to optimize performance for the specific task.
+
+## Integration
+
+This skill utilizes the `/process-vision` command provided by the computer-vision-processor plugin. It can be integrated with other skills to further process the results of the computer vision analysis, such as generating reports or triggering actions based on detected objects.

skills/computer-vision-processor/assets/README.md

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+# Assets
+
+Bundled resources for computer-vision-processor skill
+
+- [ ] example_images/: Directory containing example images for testing and demonstration purposes.
+- [ ] model_configurations/: Directory containing configuration files for different computer vision models.
+- [ ] output_templates/: Templates for generating reports or visualizations of the analysis results.

skills/computer-vision-processor/references/README.md

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+# References
+
+Bundled resources for computer-vision-processor skill
+
+- [ ] object_detection_models.md: Documentation listing available object detection models, their accuracy, and use cases.
+- [ ] image_classification_api.md: API documentation for image classification services, including request/response formats and error codes.
+- [ ] segmentation_techniques.md: Overview of different image segmentation techniques and their applications.

skills/computer-vision-processor/scripts/README.md

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+# Scripts
+
+Bundled resources for computer-vision-processor skill
+
+- [ ] image_analyzer.py: Script to perform various image analysis tasks (object detection, classification, segmentation) based on user input and specified models.
+- [ ] model_downloader.py: Script to download pre-trained computer vision models (e.g., YOLO, ResNet) from online sources.
+- [ ] format_converter.py: Script to convert images between different formats (e.g., PNG, JPG, TIFF) to ensure compatibility with analysis tools.