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Get Started Free →World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
.claude/skills/davila7-senior-computer-vision/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 238% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -16% | 0% |
World-class senior computer vision engineer skill for production-grade AI/ML/Data systems.
bash# Core Tool 1 python scripts/vision_model_trainer.py --input data/ --output results/ # Core Tool 2 python scripts/inference_optimizer.py --target project/ --analyze # Core Tool 3 python scripts/dataset_pipeline_builder.py --config config.yaml --deploy
This skill covers world-class capabilities in:
Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Comprehensive guide available in references/computer_vision_architectures.md covering:
Complete workflow documentation in references/object_detection_optimization.md including:
Technical reference guide in references/production_vision_systems.md with:
Enterprise-scale data processing with distributed computing:
Production ML system with high availability:
High-throughput inference system:
Latency:
Throughput:
Availability:
bash# Development python -m pytest tests/ -v --cov python -m black src/ python -m pylint src/ # Training python scripts/train.py --config prod.yaml python scripts/evaluate.py --model best.pth # Deployment docker build -t service:v1 . kubectl apply -f k8s/ helm upgrade service ./charts/ # Monitoring kubectl logs -f deployment/service python scripts/health_check.py
references/computer_vision_architectures.mdreferences/object_detection_optimization.mdreferences/production_vision_systems.mdscripts/ directoryAs a world-class senior professional:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | pass→pass | 4,051 | 1,958 | -52% | 1 | 1 | 0% | 689 | 1,553 | +125% | 0 | 0 | — |
case-01 | fail→pass | 7,339 | 6,491 | -12% | 1 | 1 | 0% | 1,641 | 2,293 | +40% | 0 | 0 | — |
case-02 | fail→pass | 19,779 | 25,639 | +30% | 1 | 1 | 0% | 4,011 | 6,291 | +57% | 0 | 0 | — |
case-03 | fail→pass | 6,575 | 12,252 | +86% | 1 | 1 | 0% | 1,168 | 3,944 | +238% | 0 | 0 | — |
case-04 | fail→pass | 7,413 | 2,700 | -64% | 1 | 1 | 0% | 1,370 | 1,737 | +27% | 0 | 0 | — |
case-05 | fail→pass | 9,683 | 1,751 | -82% | 1 | 1 | 0% | 1,783 | 1,496 | -16% | 0 | 0 | — |
case-06 | fail→pass | 6,782 | 1,726 | -75% | 1 | 1 | 0% | 1,140 | 1,431 | +26% | 0 | 0 | — |
case-07 | pass→pass | 10,909 | 1,577 | -86% | 1 | 1 | 0% | 1,921 | 1,401 | -27% | 0 | 0 | — |
case-08 | fail→pass | 11,617 | 1,901 | -84% | 1 | 1 | 0% | 2,075 | 1,429 | -31% | 0 | 0 | — |
case-09 | fail→pass | 19,434 | 1,583 | -92% | 1 | 1 | 0% | 1,014 | 1,408 | +39% | 0 | 0 | — |
case-10 | fail→pass | 3,396 | 1,965 | -42% | 1 | 1 | 0% | 510 | 1,545 | +203% | 0 | 0 | — |
case-11 | fail→pass | 3,951 | 1,171 | -70% | 1 | 1 | 0% | 601 | 1,329 | +121% | 0 | 0 | — |
case-13 | fail→pass | 4,396 | 1,387 | -68% | 1 | 1 | 0% | 756 | 1,399 | +85% | 0 | 0 | — |
case-14 | fail→pass | 5,900 | 1,717 | -71% | 1 | 1 | 0% | 1,162 | 1,421 | +22% | 0 | 0 | — |
case-15 | fail→pass | 10,471 | 6,450 | -38% | 1 | 1 | 0% | 2,165 | 2,408 | +11% | 0 | 0 | — |
case-16 | fail→pass | 4,149 | 2,569 | -38% | 1 | 1 | 0% | 817 | 1,581 | +94% | 0 | 0 | — |
case-22 | fail→fail | 21,624 | 19,936 | -8% | 1 | 1 | 0% | 3,686 | 5,758 | +56% | 0 | 0 | — |
case-17 | fail→pass | 5,681 | 1,438 | -75% | 1 | 1 | 0% | 1,150 | 1,411 | +23% | 0 | 0 | — |
case-18 | fail→pass | 3,538 | 1,515 | -57% | 1 | 1 | 0% | 669 | 1,433 | +114% | 0 | 0 | — |
case-19 | fail→pass | 7,904 | 1,934 | -76% | 1 | 1 | 0% | 1,544 | 1,490 | -3% | 0 | 0 | — |
case-20 | pass→pass | 11,561 | 12,267 | +6% | 1 | 1 | 0% | 2,481 | 3,608 | +45% | 0 | 0 | — |
case-21 | fail→pass | 8,612 | 7,014 | -19% | 1 | 1 | 0% | 1,473 | 2,477 | +68% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +82 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.