Build n-shot classification pipelines using Newton's Machine State Lens. Use when the user wants to classify sensor data into categories (e.g., stressed vs. relaxed, normal vs. anomaly, idle vs. active), perform anomaly detection on time-series data, or implement n-shot learning with CSV sensor data. This skill covers session lifecycle, focus CSV uploads, sliding window configuration, and SSE result parsing. Do NOT use for vision-based analysis or chart reading (use newton-activity-monitor). Do NOT use for initial API setup (use newton-setup).

NathanNam0

機器學習

Program Refactor Analysis

Refactoring analysis program. Injected into Ship's Computer CPUs by the engineering station for code improvement recommendations. Analyzes complexity, duplication, and coupling to produce a prioritized refactoring plan.

nathanvale0

機器學習

Rag Engineer

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...

ncdevshiv0

機器學習

Market Sizing Analysis

"This skill should be used when the user asks to \\\"calculate TAM\\\",

ncdevshiv0

機器學習

Vector Index Tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

ncdevshiv0

機器學習

Ollama

Use Ollama local inference for text generation, classification, and analysis via local GPU models.

ncmlabs0

機器學習

Review Circuit

Review a quantum circuit or hybrid QML block with repository-aware best practices for expressivity, gradient flow, and training stability.

necatiincekara0

機器學習

Scenario War Room

Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when facing complex risk scenarios, strategic decisions with major downside, or when the user asks 'what if X AND Y both happen?'

neekware0

機器學習

Senior Data Scientist

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

neekware0

機器學習

Senior Computer Vision Engineer

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

neekware0

機器學習

Extract

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.

neekware0

機器學習

Senior ML Engineer

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.

neekware0

機器學習

Autonomous Experiment

Autonomous experiment loop with predict-attribute cycle and multi-dimensional evaluation

nehcuh0

機器學習

Experience Evolution

Project knowledge accumulation system - learn from practice, avoid repeating mistakes

nehcuh0

機器學習

Infographic Creation

Generate a 1080x1350px LinkedIn infographic using fal.ai nano-banana-pro with brand colors and reference image style

neemydev0

機器學習

Error Recovery

Fornisce una procedura sintetica per classificare e recuperare errori operativi comuni.

Nemex810

機器學習

Laravel Models

Eloquent model patterns and database layer. Use when working with models, database entities, Eloquent ORM, or when user mentions models, eloquent, relationships, casts, observers, database entities.

nextplus8960

機器學習