aws-sagemaker
Amazon SageMaker for building, training, and deploying machine learning models. Use for SageMaker AI endpoints, model training, inference, MLOps, and AWS machine learning services.
Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Amazon SageMaker for building, training, and deploying machine learning models. Use for SageMaker AI endpoints, model training, inference, MLOps, and AWS machine learning services.
NVIDIA API documentation for integrating NVIDIA services. Use for NVIDIA NIM (NVIDIA Inference Microservices), LLM APIs, visual models, multimodal APIs, retrieval APIs, healthcare APIs, and CUDA-X microservices...
Production monitoring and observability for LLM applications with AgentOps session tracking, Google Cloud Observability integration, and Dynatrace monitoring. Provides real-time performance metrics, error track...
Detects and corrects common anti-patterns generated by Google AI Studio and similar tools.
Guides expertise system implementation: multi-path recognition, hierarchy (local → city → state → national), golden experts, visit quality. Use when implementing expertise calculations, expert recognition, or e...
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Enforces AI2AI privacy validation: anonymized data exchange, no personal identifiers, privacy-preserving learning. Use when implementing AI2AI features, validating privacy, or ensuring privacy compliance.
Expert intégration IA/ML dans applications web - LLMs, RAG, vector databases, embeddings. Utilise ce skill quand: (1) intégration API OpenAI/Claude/Mistral, (2) mise en place RAG, (3) vector databases, (4) prom...
Calculate and track key performance indicators (KPIs) from pilot data and defined metrics. Use when deriving success metrics, benchmarking performance against objectives, or preparing quantitative reports and d...
Audit pilot data, models, and workflows for bias and inclusivity issues. Use when evaluating fairness across demographic groups, identifying algorithmic biases, and recommending mitigation strategies to ensure...
Run accuracy evaluation/tuning for RQ-SCAN and generate reports.
best practices for defining MCP tools using JSON schemas. Use this to design robust `inputSchema`, `outputSchema`, and `annotations` that guide the model effectively.
State-of-the-art machine learning for large-scale tabular data (millions of rows, thousands of features). Use when building predictive models on tabular/structured data including regression and classification t...
Prompt Engineering avanzado 2025 - Context Engineering, DSPy, optimización de tokens y seguridad.
Advanced 2026 context curation patterns (Nvidia Eureka, Context Hygiene, RLMF) for autonomous agents.
Data analysis workflows and patterns for processing, analyzing, and visualizing data using Python data science libraries.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create...
Unified learning skill - log decisions (--decision), review launches (--launch), or extract patterns from history (--patterns)
Unified analysis skill - Python data analysis (--data) or KB gap identification (--kb)
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt temp...
C4 model architecture diagrams
Work with the spaced repetition learning algorithm and review system
Reproduces the full prefill sensitivity analysis pipeline for reward hacking indicators. Use when evaluating how susceptible model checkpoints are to exploit-eliciting prefills, computing token-based trajectori...
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