computer-vision-engineering
v0.1.2Computer-vision engineering team — agents for a production vision build: cv-systems-architect (task framing across classification / detection / segmentation / OCR / tracking / VLM, data & annotation strategy, build-vs-API and deployment-target choice, eval-metric design — mAP / IoU / precision-recall), cv-model-engineer (dataset curation, augmentation, transfer learning, model selection across YOLO / DETR / SAM / CLIP / ViT, active learning, the eval harness, drift), and vision-deployment-engineer (quantization / distillation, ONNX / TensorRT / CoreML / TFLite export, edge/embedded targets, streaming-video pipelines, latency). Vision-specific vs the MLOps-broad ml-engineering. Ships skills, a knowledge bank (Mermaid decision trees + a dated 2026 reference), best-practices, templates, commands. Engineering judgment, not a benchmark verdict; model/hardware/runtime specifics carry a retrieval date + [verify-at-use]; no PII, no image data stored. Requires ravenclaude-core@>=0.7.0.
By Matt CorbettLicense: MIT7 GitHub starsUpdated last week
Directory evidence
- Runtimes
- Claude Code
- Parsed components
- 4 skill or MCP entries
- Source updated
- Sep 15, 2026
- Manifest status
- Canonical path parsed
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Install computer-vision-engineering for Claude Code
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install computer-vision-engineering@agent-plugin-marketplacePaste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/mcorbett51090/RavenClaudeClone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/computer-vision-engineering/.
Plugin files
├── .claude-plugin/plugin.json├── skills/cv-model-training-and-evaluation/SKILL.md├── skills/cv-task-and-data-strategy/SKILL.md├── skills/video-pipeline-and-edge-deployment/SKILL.md└── skills/vision-inference-optimization/SKILL.md
Included Skills4
Curate and augment the dataset, choose and fine-tune the model (YOLO / DETR / SAM / CLIP / EfficientNet / ViT) on the task and target budget, design the loss and metric to match the cost, spend the annotation budget with active learning, handle class imbalance, and build an eval harness you can trust with per-slice metrics and drift detection. Model/checkpoint specifics verify-at-use; no PII.
Frame the computer-vision task (classification / detection / segmentation / OCR / pose / tracking / VLM) on the decision the system must make, choose the metric that mirrors the business cost, decide build-vs-fine-tune-vs-API jointly with the deployment target, and design the data & annotation strategy. Model/metric specifics verify-at-use; no PII, no image data stored.
Design streaming-video pipelines that hold the frame budget — frame sampling / keyframe strategy, ROI cropping, tracking-by-detection so the detector doesn't run every frame, and batching where latency allows — and deploy to edge/embedded targets (Jetson, mobile NPU, Coral) with the camera/sensor capture and pre-processing counted inside the budget. Device numbers verify-at-use; no PII.
Make a trained vision model fit and run on its target: budget latency on the real device, optimize in order of leverage (quantization INT8/FP16 with calibration, then pruning, then distillation), export to the runtime the target uses (ONNX / TensorRT / CoreML / TFLite / OpenVINO), and re-check accuracy against the operating point after every step. Device/runtime numbers verify-at-use; no PII.
Plugin manifests1
{
"name": "computer-vision-engineering",
"version": "0.1.2",
"description": "Computer-vision engineering team — agents for a production vision build: cv-systems-architect (task framing across classification / detection / segmentation / OCR / tracking / VLM, data & annotation strategy, build-vs-API and deployment-target choice, eval-metric design — mAP / IoU / precision-recall), cv-model-engineer (dataset curation, augmentation, transfer learning, model selection across YOLO / DETR / SAM / CLIP / ViT, active learning, the eval harness, drift), and vision-deployment-engineer (quantization / distillation, ONNX / TensorRT / CoreML / TFLite export, edge/embedded targets, streaming-video pipelines, latency). Vision-specific vs the MLOps-broad ml-engineering. Ships skills, a knowledge bank (Mermaid decision trees + a dated 2026 reference), best-practices, templates, commands. Engineering judgment, not a benchmark verdict; model/hardware/runtime specifics carry a retrieval date + [verify-at-use]; no PII, no image data stored. Requires ravenclaude-core@>=0.7.0.",
"author": {
"name": "Matt Corbett"
},
"homepage": "https://github.com/mcorbett51090/RavenClaude",
"license": "MIT",
"keywords": [
"computer-vision",
"object-detection",
"image-segmentation",
"ocr",
"video-analytics",
"edge-inference",
"tensorrt",
"onnx",
"yolo",
"vision-transformer",
"foundation-models",
"model-quantization",
"mlops-vision",
"active-learning",
"camera-pipeline"
],
"requires": {
"plugins": [
"ravenclaude-core@>=0.7.0"
]
}
}For maintainers
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[computer-vision-engineering on Agent Plugins Marketplace](https://pluginsmp.com/plugins/computer-vision-engineering)