processing-computer-vision-tasks
Process images using object detection, classification, and segmentation. Use when requesting "analyze image", "object detection", "image classification", or "computer vision". Trigger with relevant phrases based on skill purpose.
What this skill does
# Computer Vision Processor Process images using object detection, classification, and segmentation pipelines with configurable model backends. ## 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. ## Prerequisites - Appropriate file access permissions - Required dependencies installed ## Instructions 1. Invoke this skill when the trigger conditions are met 2. Provide necessary context and parameters 3. Review the generated output 4. Apply modifications as needed ## Output The skill produces structured output relevant to the task. ## Error Handling - Invalid input: Prompts for correction - Missing dependencies: Lists required components - Permission errors: Suggests remediation steps ## Resources - Project documentation - Related skills and commands
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
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humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
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