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/ricneves-ai/flowgrammers-skillsnpx agentmods add skills/ricneves-ai/flowgrammers-skills/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/ricneves-ai/flowgrammers-skills/senior-computer-vision)<a href="https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/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/ricneves-ai/flowgrammers-skills/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/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 | $0.00114 | $0.04148 |
| Opus 5 | $0.00057 | $0.02074 |
| Sonnet 5 | $0.00023 | $0.00830 |
| Haiku 4.5 | $0.00011 | $0.00415 |
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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engenheiro Sênior de Visão Computacional
Skill de engenharia de visão computacional em produção para detecção de objetos, segmentação de imagens e implantação de sistemas de IA visual.
Sumário
- Início Rápido
- Especialização Principal
- Tech Stack
- Fluxo de Trabalho 1: Pipeline de Detecção de Objetos
- Fluxo de Trabalho 2: Otimização e Implantação de Modelo
- Fluxo de Trabalho 3: Preparação de Dataset Personalizado
- Guia de Seleção de Arquitetura
- Documentação de Referência
- Comandos Comuns
Início Rápido
# Gerar configuração de treinamento para YOLO ou Faster R-CNN
python scripts/vision_model_trainer.py models/ --task detection --arch yolov8
# Analisar modelo para oportunidades de otimização (quantização, pruning)
python scripts/inference_optimizer.py model.pt --target onnx --benchmark
# Construir pipeline de dataset com augmentações
python scripts/dataset_pipeline_builder.py images/ --format coco --augment
Especialização Principal
Esta skill fornece orientação sobre:
- Detecção de Objetos: Família YOLO (v5-v11), Faster R-CNN, DETR, RT-DETR
- Segmentação de Instâncias: Mask R-CNN, YOLACT, SOLOv2
- Segmentação Semântica: DeepLabV3+, SegFormer, SAM (Segment Anything)
- Classificação de Imagens: ResNet, EfficientNet, Vision Transformers (ViT, DeiT)
- Análise de Vídeo: Rastreamento de objetos (ByteTrack, SORT), reconhecimento de ação
- Visão 3D: Estimativa de profundidade, processamento de nuvem de pontos, NeRF
- Implantação em Produção: ONNX, TensorRT, OpenVINO, CoreML
Tech Stack
| Categoria | Tecnologias |
|---|---|
| Frameworks | PyTorch, torchvision, timm |
| Detecção | Ultralytics (YOLO), Detectron2, MMDetection |
| Segmentação | segment-anything, mmsegmentation |
| Otimização | ONNX, TensorRT, OpenVINO, torch.compile |
| Processamento de Imagem | OpenCV, Pillow, albumentations |
| Anotação | CVAT, Label Studio, Roboflow |
| Rastreamento de Experimentos | MLflow, Weights & Biases |
| Serving | Triton Inference Server, TorchServe |
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 · 442 lines · 114 tokens per session scan A 3e899e0661a2
senior-computer-vision is a skill published in the GitHub repository ricneves-ai/flowgrammers-skills (112 stars, last pushed 3mo ago), licensed MIT. It adds 114 tokens to every session and 4,148 once invoked, about $0.0006 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 2026-09-03.
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