Comprehensive deep learning guidelines for neural network development, training, and optimization.
Use when building any system that involves AI/model calls - integrates with brainstorming, planning, and TDD to ensure model agency over hardcoded rules
Access LinkedIn Learning courses and track professional development
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理
Summarize what was learned in the conversation into markdown format and post it to the local API endpoint
Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.
Use this skill when users need to stress test their business model, identify scale limitations, find bottlenecks, determine if they're trading time for money, or evaluate unit economics. Activates...
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Expert in complex state management with Finite State Machines, XState patterns, state persistence, race condition handling, and distributed state synchronization. Use for managing complex...
Design and implement agent-based models (ABM) for simulating complex systems with emergent behavior from individual agent interactions. Use when "agent-based, multi-agent, emergent behavior, swarm...
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Design personalized learning plans for skills, topics, and career development
Automatically discover machine learning and AI skills when working with machine learning. Activates for ml development tasks.
Design data models with Pydantic schemas, comprehensive validation rules,
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
Skip to content github / docs Code Issues 80 Pull requests 35 Discussions Actions Projects 2 Security Insights Merge branch 'main' into 1862-Add-Travis-CI-migration-table ...