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    Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.
    20
    35 npm
    12
    MIT
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    Forces host coding agents through a structured multi-step reasoning protocol, scaffolding, validating, and formatting their thinking in QUICK, STANDARD, or DEEP modes, plus independent refutation and self-consistency verification passes. It runs entirely locally over stdio or Streamable HTTP using bash with no LLM, API key, GPU, or inference cost.
    MIT
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    Enables dynamic and reflective problem-solving through a structured thinking process with tools for sequential analysis, tree of thoughts, self-critique, and integration with external knowledge and codebase analysis.
    7 npm
    MIT
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    Facilitates structured creative thinking through sequential thought processing, helping users shift from reactive problem-solving to proactive outcome creation using structural tension analysis and stage-based thinking workflows.
    1
    MIT
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    Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.
    1
    7 npm
    4
    MIT
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    A tool that implements Claude Shannon's problem-solving methodology to help break down complex problems into structured steps including problem definition, constraints, modeling, validation, and implementation.
    1
    32 npm
    70
    MIT
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    An MCP server and CLI tool that transforms codebases into AI-ready context through semantic search, call graph analysis, and incremental indexing. It enables AI assistants to perform hybrid vector and keyword searches to understand complex repository structures and cross-file relationships.
    35
    41 npm
    1
    MIT
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    Facilitates structured, progressive thinking through defined cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), enabling users to break down complex problems, track thought progression, and generate summaries of their thinking process.
    MIT
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    Provides AI agents with data validation, transformation, and normalization capabilities, including JSON schema validation, CSV processing, data normalization, text cleaning, and dataset merging.
    5
    43 npm
    MIT
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    Provides advanced probabilistic decision-making algorithms including MDPs, MCTS, Multi-Armed Bandits, Bayesian Optimization, and Hidden Markov Models to help AI assistants explore alternative solutions and optimize long-term decisions.
    1
    6
    MIT