Senior Computer Vision
alirezarezvani/claude-skills
Computer vision engineering skill for object detection, image segmentation, and visual AI systems.
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment.
Install Senior Computer Vision
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills senior-computer-vision --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/senior-computer-vision .claude/skills/senior-computer-vision && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .claude/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-visionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills senior-computer-vision --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/senior-computer-vision .agents/skills/senior-computer-vision && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .agents/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills senior-computer-vision --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/senior-computer-vision .cursor/skills/senior-computer-vision && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .cursor/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path engineering/senior-computer-vision--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills senior-computer-vision --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/senior-computer-vision .gemini/skills/senior-computer-vision && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .gemini/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills senior-computer-visionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/senior-computer-vision .github/skills/senior-computer-vision && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .github/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-computer-vision -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills senior-computer-vision --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/senior-computer-vision .opencode/skills/senior-computer-vision && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "senior-computer-vision" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-computer-vision into .opencode/skills/senior-computer-vision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-computer-vision", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
senior-computer-vision- GitHub stars
- 874
- Token cost
- ~1.8k tokens
- SKILL.md length
- 650 words
- Files
- 10 (incl. scripts, references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment.
- Building detection pipelines
- SKILL.md covers Core Capabilities, When to Use, Clarify First and Tools, plus 3 more sections
- Runs Python scripts from its folder; calls python
- Training models
What it does
Senior Computer Vision is an agent skill from borghei/Claude-Skills. Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `references/commands-targets-and-troubleshooting.md`, `references/computer_vision_architectures.md` and `references/detection-workflows.md`).
It sits in AI & LLM Engineering, covering Computer vision. It works with NVIDIA AI Platform and ONNX. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Building detection pipelines
- Training models
- Optimizing inference
Example prompts
- “/senior-computer-vision”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Senior Computer Vision loads about 1.8k tokens when it runs, and up to ~26k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 650 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 650 words, ~1,754 tokens.
Download SKILL.mdSave it as
.claude/skills/senior-computer-vision/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- senior-computer-vision
- description
- Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
- license
- MIT + Commons Clause
- metadata.version
- 1.1.0
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- computer-vision
- metadata.updated
- 2026-06-17
- metadata.tags
- object-detection, image-segmentation, computer-vision, model-training
Senior Computer Vision Engineer
Design end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.
Core Capabilities
- Detection pipeline design — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.
- Model optimization & deployment — baseline benchmarking, ONNX export, INT8/FP16 quantization, and conversion to TensorRT/OpenVINO/CoreML/TFLite per target platform.
- Dataset engineering — audit, cleaning, format conversion (COCO/YOLO/VOC/CVAT/LabelMe), augmentation config, and stratified train/val/test splits.
- Architecture guidance — detection and segmentation architecture trade-offs plus CNN vs Vision Transformer selection.
- Production targets — FPS, mAP, latency P99, memory, and model-size budgets for real-time, high-accuracy, and edge deployments.
When to Use
- Building an object detection or segmentation system from scratch.
- Optimizing and deploying a trained model to GPU, edge, or mobile.
- Preparing, converting, or auditing a computer vision dataset.
- Choosing an architecture for a speed/accuracy/deployment trade-off.
Clarify First
Before generating training configs or pipelines, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Task — detection / instance or semantic segmentation / classification (selects the architecture and
--task) - Dataset — location and format (COCO / YOLO / VOC) to analyze or convert (the input to
dataset_pipeline_builder.py) - Deployment target — GPU / edge / mobile (drives architecture choice and
inference_optimizer --target)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool | Purpose | Command |
|---|---|---|
vision_model_trainer.py | Generate training configs for YOLO / Detectron2 / MMDetection | python scripts/vision_model_trainer.py data/coco/ --task detection --arch yolov8m -o configs/train.yaml |
inference_optimizer.py | Analyze, benchmark, and recommend optimizations for a model | python scripts/inference_optimizer.py model.pt --analyze --benchmark --recommend --target edge |
dataset_pipeline_builder.py | Analyze/convert/split/augment/validate CV datasets (subcommands) | python scripts/dataset_pipeline_builder.py analyze --input data/coco/ |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/detection-workflows.md — quick-start commands and the three end-to-end workflows (detection pipeline, model optimization/deployment, dataset prep) plus the architecture selection guide. Read when executing a pipeline.
- references/commands-targets-and-troubleshooting.md — framework command catalogs (YOLO/Detectron2/MMDetection/optimization), performance targets, anti-patterns, troubleshooting table, and success criteria. Read while running training or deployment.
- references/tool-reference.md — full parameter, example, and output-format reference for the three scripts. Read when scripting the tools.
- references/computer_vision_architectures.md — CNN backbones (ResNet, EfficientNet, ConvNeXt), ViT variants (ViT, DeiT, Swin), detection heads, and FPN/BiFPN/PANet necks. Read when choosing or tuning architectures.
- references/object_detection_optimization.md — NMS variants, anchor optimization, loss design (focal, GIoU/CIoU/DIoU), training strategies, and detection augmentation. Read when improving detection accuracy.
- references/production_vision_systems.md — ONNX/TensorRT export, batch inference, edge deployment (Jetson, Intel NCS), Triton serving, and video pipelines. Read when deploying to production.
Show full SKILL.md (226 more words)Show less
Scope & Limitations
This skill covers:
- End-to-end object detection and segmentation pipeline design (data preparation through production deployment)
- Training configuration generation for Ultralytics YOLO, Detectron2, and MMDetection frameworks
- Model optimization and export to ONNX, TensorRT, OpenVINO, and CoreML runtimes
- Dataset format conversion (COCO, YOLO, Pascal VOC, CVAT), splitting, validation, and augmentation configuration
This skill does NOT cover:
- Generative vision tasks (image generation, style transfer, super-resolution) -- see dedicated generative AI skills
- 3D reconstruction, SLAM, or point cloud processing beyond basic depth estimation
- Medical imaging regulatory compliance (DICOM, FDA 510(k)) -- see
ra-qm-team/compliance skills - Real-time video streaming infrastructure (RTSP, WebRTC, GStreamer pipeline design) -- see
senior-devopsfor infrastructure
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
senior-ml-engineer | Model serving and MLOps pipeline setup | Trained model artifacts (.pt, .onnx) flow into model_deployment_pipeline.py for containerized serving and monitoring |
senior-data-engineer | Dataset ETL and storage pipelines | Raw image data ingested via pipeline_orchestrator.py; cleaned datasets flow into dataset_pipeline_builder.py for CV formatting |
senior-data-scientist | Experiment design and statistical analysis | Experiment parameters from experiment_designer.py guide hyperparameter search; model metrics feed back for significance testing |
senior-devops | CI/CD and GPU infrastructure provisioning | Optimized model artifacts deployed via CI/CD pipelines; GPU node scaling managed through infrastructure-as-code |
senior-prompt-engineer | Multimodal RAG and vision-language integration | Vision model embeddings and detections feed into rag_system_builder.py for multimodal retrieval pipelines |
senior-cloud-architect | Cloud GPU resource planning and cost optimization | Benchmark results from inference_optimizer.py inform instance type selection and auto-scaling policies |
© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 9 other files (scripts, references) in engineering/senior-computer-vision of borghei/Claude-Skills.
- SKILL.md
- references/commands-targets-and-troubleshooting.md
- references/computer_vision_architectures.md
- references/detection-workflows.md
- references/object_detection_optimization.md
- references/production_vision_systems.md
- references/tool-reference.md
- scripts/dataset_pipeline_builder.py
- scripts/inference_optimizer.py
- scripts/vision_model_trainer.py
Open the folder on GitHubat commit c9a1487
Compare with similar skills
Senior Computer Vision next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Senior Computer Vision this skillborghei/Claude-Skills | 874 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Senior Computer Visionalirezarezvani/claude-skills | 28k | 2 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Deepstream Import Vision ModelNVIDIA/skills | 3.5k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Tao Finetune ClipNVIDIA/skills | 3.5k | — | ~4k | Automated safety check: Notes | Apache-2.0 | |
| Tao Port Huggingface ModelNVIDIA/skills | 3.5k | — | ~4.5k | Automated safety check: Notes | Apache-2.0 | |
| Computer Vision Engineertheneoai/awesome-skills | 183 | — | ~2.2k | Automated safety check: Pass | MIT |
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Questions about Senior Computer Vision
What does Senior Computer Vision do?
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Senior Computer Vision is an agent skill from borghei/Claude-Skills. Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment.
When should I use Senior Computer Vision?
Senior Computer Vision fits situations like: building detection pipelines; training models; optimizing inference.
How do I install Senior Computer Vision in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill senior-computer-vision -a claude-code`. Or copy the skill folder (engineering/senior-computer-vision in borghei/Claude-Skills) into .claude/skills/senior-computer-vision in your project. Claude Code loads it when a task matches its description.
How do I install Senior Computer Vision in Codex?
Run `npx skills add borghei/Claude-Skills --skill senior-computer-vision -a codex`. Or copy the skill folder (engineering/senior-computer-vision in borghei/Claude-Skills) into .agents/skills/senior-computer-vision in your project. Codex loads it when a task matches its description.
Can I use Senior Computer Vision in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill senior-computer-vision -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-computer-vision, .gemini/skills/senior-computer-vision, .github/skills/senior-computer-vision and .opencode/skills/senior-computer-vision in your project.
What does Senior Computer Vision need to run?
Going by SKILL.md and its folder, Senior Computer Vision needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Senior Computer Vision access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Senior Computer Vision safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Senior Computer Vision use?
Senior Computer Vision is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Senior Computer Vision use?
About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 24k tokens, read only when the agent opens those files.
What are the alternatives to Senior Computer Vision?
Skills that share tags, products or a category with Senior Computer Vision: Senior Computer Vision (alirezarezvani/claude-skills, 28k stars), Deepstream Import Vision Model (NVIDIA/skills, 3.5k stars), Tao Finetune Clip (NVIDIA/skills, 3.5k stars) and Tao Port Huggingface Model (NVIDIA/skills, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Senior Computer Vision?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.