Applies the framework from Stuart Russell & Peter Norvig's "Artificial Intelligence: A Modern Approach" (AIMA) — the rational-agent model (PEAS, environment properties), search (uninformed, informed/heuristic A, adversarial minimax/alpha-beta), constraint satisfaction, knowledge representation and logical inference, reasoning under uncertainty (Bayesian networks, Markov models), and Markov Decision Processes / reinforcement learning — to problems that involve designing an agent, choosing a search or planning algorithm, modeling uncertainty, or picking the right AI technique for a task. Use whenever the user wants an "AI: a modern approach" or "AIMA" lens, mentions "rational agent," "PEAS," "heuristic search," "A search," "minimax," "alpha-beta pruning," "constraint satisfaction," "Bayesian network," "Markov decision process," or is deciding which class of algorithm/approach fits an AI or search/optimization problem — even if they don't name the book.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryGeneralStars0
About this skill
Applies the framework from Stuart Russell & Peter Norvig's "Artificial Intelligence: A Modern Approach" (AIMA) — the rational-agent model (PEAS, environment properties), search (uninformed, informed/heuristic A, adversarial minimax/alpha-beta), constraint satisfaction, knowledge representation and logical inference, reasoning under uncertainty (Bayesian networks, Markov models), and Markov Decision Processes / reinforcement learning — to problems that involve designing an agent, choosing a search or planning algorithm, modeling uncertainty, or picking the right AI technique for a task. Use whenever the user wants an "AI: a modern approach" or "AIMA" lens, mentions "rational agent," "PEAS," "heuristic search," "A search," "minimax," "alpha-beta pruning," "constraint satisfaction," "Bayesian network," "Markov decision process," or is deciding which class of algorithm/approach fits an AI or search/optimization problem — even if they don't name the book.
Quick start
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Applies the framework from Stuart Russell & Peter Norvig's "Artificial Intelligence: A Modern Approach" (AIMA) — the rational-agent model (PEAS, environment properties), search (uninformed, informed/heuristic A*, adversarial minimax/alpha-beta), constraint satisfaction, knowledge representation and logical inference, reasoning under uncertainty (Bayesian networks, Markov models), and Markov Decision Processes / reinforcement learning — to problems that involve designing an agent, choosing a search or planning algorithm, modeling uncertainty, or picking the right AI technique for a task. Use whenever the user wants an "AI: a modern approach" or "AIMA" lens, mentions "rational agent," "PEAS," "heuristic search," "A* search," "minimax," "alpha-beta pruning," "constraint satisfaction," "Bayesian network," "Markov decision process," or is deciding which class of algorithm/approach fits an AI or search/optimization problem — even if they don't name the book.
About this listing
Category
General
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI