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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, impleme...
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Integrate with Replicate AI for running models (image generation, LLMs, etc.).
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cogn...
Implement machine learning solutions including model architectures, training pipelines, optimization strategies, and performance improvements. This skill spawns a specialist ML implementation agent...
Visualize datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when users want to explore dataset structure, find clusters in images, identify outliers, color samples by class or met...
Train AI agents using AgentDB's 9 reinforcement learning algorithms including Q-Learning, DQN, PPO, and Actor-Critic. Build self-learning agents, implement RL training loops with experience replay, and deploy o...
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement
Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with eviden...
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learnin...
Debug ML training issues and optimize performance including loss divergence, overfitting, and slow convergence
Extract patterns and generalizations from multiple observations. Use when detecting recurring themes, building predictive rules, or identifying systemic behaviors from accumulated data. Produces validated patte...
Implement ReasoningBank adaptive learning with AgentDB for trajectory tracking, verdict judgment, memory distillation, and pattern recognition to build self-learning agents that improve decision-making through...
Fetch current model names from AI providers (Anthropic, OpenAI, Gemini, Ollama), classify them into tiers (fast/default/heavy), and detect new models. Use when needing up-to-date model IDs for API calls or when...
Prevents 30+ critical AI/ML mistakes including data leakage, evaluation errors, training pitfalls, and deployment issues. Use when working with ML training, testing, model evaluation, or deployment.
Skill for managing model provider priorities with authentication (OAuth/Subscription/API), usage limits, and automatic fallback across all major AI providers
Active diagnostic tool for analyzing prompt quality, detecting anti-patterns, identifying token waste, and providing optimization recommendations
This SOP provides a systematic workflow for training and deploying neural networks using Flow Nexus platform with distributed E2B sandboxes. It covers architecture selection, distributed training, ...
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for...
Create a FiftyOne dataset from a directory of media files (images, videos, point clouds), optionally import labels in common formats (COCO, YOLO, VOC), run model inference, and store predictions. Use when users...
Specialized ML model development, training, and deployment workflow
Expert at creating continuous improvement feedback loops for Claude's responses. Use when establishing self-improvement processes, tracking progress over time, or implementing iterative refinement workflows.
Design and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts tha...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.