MCP App StoreMCP App Store
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Build and analyze discrete or continuous Markov chains: transition matrices, state classification, stationary distributions, and mean first-passage times.
Provides end-to-end workflows for constructing, classifying, and analyzing discrete-time and continuous-time Markov chains from observed transition data. Produces transition matrices or generators, classifies states (transient/recurrent/absorbing), computes stationary distributions, mean first-passage times, and supports simulation-based validation.
Use this skill when modeling systems with the Markov property from observed transitions or rates—e.g., reliability analysis, queuing systems, user-behavior modeling, or as a foundation for HMM/MDP workflows. Triggers include requests to compute stationary distributions, absorption probabilities, or to validate analytic results with simulation.
Suitable for analytic and data-focused agents (Python-capable assistants, scientific computation agents, Claude/Codex-style agents with numerical libraries).
A well-structured Chinese-language (zh-CN) skill for building and analyzing discrete or continuous Markov chains, covering transition matrices, state classification, stationary distributions, and mean first-passage times. Detailed steps with expected results and failure handling for each phase, plus verification checklists and common pitfalls. No bundled scripts — purely instructional. Niche mathematical domain limits broad applicability.
Clean, mathematical skill with no security concerns. Translation from English (source_commit: a87e5e03) by claude on 2026-03-17. Good pedagogical structure with validation checkpoints at each step.
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