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Interactively explore PDDL planning problems, execute actions, and verify states and goals.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "pypddlengine" yet — see the docs or source repo.
Initialize this PDDL domain and problem, list the executable actions in the current state, then show the new state after each action I take and explain which preconditions failed if an action cannot execute.
Returns the initialization result, currently executable actions, state changes after each step, and explanations for failed actions.
After executing the following action sequence, check whether the current state satisfies the goal conditions; if not, identify which facts are still missing.
Provides the post-execution state, a goal satisfaction verdict, and a list of unmet goal conditions.
Use this sample PDDL problem for a teaching demo: initialize it, then execute a feasible action sequence step by step, explaining how the state changes and why each action is applicable at that moment.
Produces a learner-friendly step-by-step planning walkthrough with action applicability and state transition explanations.
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Equip AI with mechanical engineering tools for calculations, conversions, and analysis.
Give AI coding agents persistent product context for more consistent development.
Search Pinduoduo products, normalize listings, and analyze authenticity risk signals.
Combine Prolog reasoning with MCP to power hybrid AI applications.
Give AI agents pay-per-use scraping, PDF parsing, OCR, and more.