Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/yezannnnn/agentGroupnpx agentmods add skills/yezannnnn/agentgroup/senior-computer-visionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yezannnnn/agentgroup/senior-computer-vision)<a href="https://agentmods.dev/skills/yezannnnn/agentgroup/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/senior-computer-vision/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yezannnnn/agentgroup/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/senior-computer-vision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.04251 |
| Opus 5 | $0.00048 | $0.02125 |
| Sonnet 5 | $0.00019 | $0.00850 |
| Haiku 4.5 | $0.00010 | $0.00425 |
Grade A, and why
senior-computer-vision scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 532 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Computer Vision Engineer
Production computer vision engineering skill for object detection, image segmentation, and visual AI system deployment.
Table of Contents
- Quick Start
- Core Expertise
- Tech Stack
- Workflow 1: Object Detection Pipeline
- Workflow 2: Model Optimization and Deployment
- Workflow 3: Custom Dataset Preparation
- Architecture Selection Guide
- Reference Documentation
- Common Commands
Quick Start
# Generate training configuration for YOLO or Faster R-CNN
python scripts/vision_model_trainer.py models/ --task detection --arch yolov8
# Analyze model for optimization opportunities (quantization, pruning)
python scripts/inference_optimizer.py model.pt --target onnx --benchmark
# Build dataset pipeline with augmentations
python scripts/dataset_pipeline_builder.py images/ --format coco --augment
Core Expertise
This skill provides guidance on:
- Object Detection: YOLO family (v5-v11), Faster R-CNN, DETR, RT-DETR
- Instance Segmentation: Mask R-CNN, YOLACT, SOLOv2
- Semantic Segmentation: DeepLabV3+, SegFormer, SAM (Segment Anything)
- Image Classification: ResNet, EfficientNet, Vision Transformers (ViT, DeiT)
- Video Analysis: Object tracking (ByteTrack, SORT), action recognition
- 3D Vision: Depth estimation, point cloud processing, NeRF
- Production Deployment: ONNX, TensorRT, OpenVINO, CoreML
Tech Stack
| Category | Technologies |
|---|---|
| Frameworks | PyTorch, torchvision, timm |
| Detection | Ultralytics (YOLO), Detectron2, MMDetection |
| Segmentation | segment-anything, mmsegmentation |
| Optimization | ONNX, TensorRT, OpenVINO, torch.compile |
| Image Processing | OpenCV, Pillow, albumentations |
| Annotation | CVAT, Label Studio, Roboflow |
| Experiment Tracking | MLflow, Weights & Biases |
| Serving | Triton Inference Server, TorchServe |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 532 lines · 97 tokens per session scan A 6fb196ef397a
senior-computer-vision is a skill published in the GitHub repository yezannnnn/agentGroup (149 stars, last pushed 3mo ago), licensed MIT. It adds 97 tokens to every session and 4,251 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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