pufferlib
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
simpy
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
pymatgen
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
thrivve-mc-when
Thrivve Partners Monte Carlo simulation to forecast completion date based on remaining work and historical throughput. Use when the user asks "when will I complete [N] stories/tasks" with historical daily throughput data. Requires at least 10 days of throughput history, a count of remaining items, and optional confidence level (default 85%).
thrivve-mc-how-many
Thrivve Partners Monte Carlo simulation to forecast story/task completion based on historical throughput. Use when the user asks "how many stories/tasks will be completed by [date]" with historical daily throughput data. Requires at least 10 days of throughput history and a future target date. Provides probabilistic forecasts at specified confidence levels (default 85%).
scenario-analyzer
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simulation-speed-adjustment
Adjust simulation temporal processing speed.
whole-brain-emulation-core-simulation
Simulate whole-brain emulation core processes.
computational-model-design
Design computational models for cognitive simulation and analysis.
gtnh-skill
This skill provides accurate data about GT New Horizons based on official data dumps. Use when answering questions about GTNH (GregTech New Horizons).
rosetta-helix-substrate
Consciousness simulation framework with Kuramoto oscillators, APL operators, and K-formation dynamics. Use for physics simulations, phase transitions, coherence analysis, and cloud training via GitHub Actions. Requires numpy and requests packages.
game-loop
Server-side game loop implementation with fixed timestep, physics simulation, and tick rate optimization
healthsim-rxmembersim
RxMemberSim generates realistic synthetic pharmacy data for testing PBM systems, claims adjudication, and drug utilization review. Use when user requests: (1) pharmacy claims or prescription data, (2) DUR alerts or drug interactions, (3) formulary or tier cohorts, (4) pharmacy prior authorization, (5) NCPDP formatted output.
healthsim-populationsim
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healthsim-membersim
MemberSim generates realistic synthetic claims and payer data for testing claims processing systems, payment integrity, and benefits administration.
healthsim-networksim
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