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Found 171 Skills
Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
Use this skill when managing production incidents, designing on-call rotations, writing runbooks, conducting post-mortems, setting up status pages, or running war rooms. Triggers on incident response, incident commander, on-call schedule, pager escalation, runbook authoring, post-incident review, blameless retro, status page updates, war room coordination, severity classification, and any task requiring structured incident lifecycle management.
DORA (EU 2022/2554) digital operational resilience compliance automation for financial entities. Assesses readiness against all 5 DORA pillars, classifies ICT incidents, validates third-party risk management, and generates resilience testing plans. Use for DORA compliance assessments, ICT risk management, incident classification, third-party ICT oversight, and digital operational resilience testing.
Neo4j Graph Data Science (GDS) plugin — graph projection, algorithm execution, execution modes (stream/stats/mutate/write), memory estimation, and the GDS Python client (graphdatascience v1.21). Use when running gds.pageRank, gds.louvain, gds.wcc, gds.fastRP, gds.knn, gds.betweenness, gds.nodeSimilarity, or any gds.* procedure; projecting named in-memory graphs with gds.graph.project or graph.project; chaining algorithms with mutate mode; computing node embeddings for ML; building recommendation systems with FastRP + KNN. Also triggers on GraphDataScience, GdsSessions, graph catalog operations, ML pipelines, node classification, link prediction. Does NOT cover Aura Graph Analytics serverless sessions — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.
Map environmental/industrial chemicals to mechanistic adverse outcome pathways (AOPs) using AOPWiki, quantify toxicological hazard (PubChemTox GHS/carcinogen classification, LD50 values), and link chemical stressors to gene targets and disease endpoints via CTD for regulatory risk assessment. Use when asked about AOP stressor mapping, GHS hazard categories, LD50 data, IARC carcinogen classification, or mechanism-based risk assessment for non-drug chemicals.
Performs gap analysis on NVIDIA TAO Visual ChangeNet (VCN) Classify experiments by invoking the data-services container (`tao_toolkit.data_services` from `versions.yaml`) directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, auditing PASS/NO_PASS boundary cases, or running DEFT gap analysis on an AOI ChangeNet model.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.