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    Provides access to Brian Eno and Peter Schmidt's Oblique Strategies card deck to help users overcome creative blocks through lateral thinking. It enables searching and retrieving random prompts from various editions, including collections adapted specifically for programmers.
    Last updated
    3
    1
  • A
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    MCP proxy that bundles flat tool lists into hierarchical subcommand groups, reducing context token usage and improving tool discovery.
    Last updated
    18
    5
    MIT
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    Analyzes, generates, simulates, and optimizes caching strategies across HTTP, CDN, and browser layers for AI agents.
    Last updated
    46
    MIT
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    A standalone MCP Tool Registry server providing generic tool search and discovery for MCP clients, reducing token consumption and improving tool selection accuracy.
    Last updated
    MIT
  • A
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    quality
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    Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.
    Last updated
    8
    2
    Apache 2.0
  • A
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    quality
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    maintenance
    A self-improving knowledge agent that provides local retrieval, web research, and structured answer synthesis for Claude Code and VS Code Copilot via MCP. It enables AI tools to manage a project-specific knowledge lifecycle through automated research and indexing.
    Last updated
    12
    9
    MIT
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    MCP server for the FLOX trading framework. About 30 tools to run backtests, scaffold strategies, validate for lookahead bias, compute indicators, place orders, and query PnL from Claude/Cursor.
    Last updated
    37
    220
    MIT
  • A
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    Advanced cognitive thinking MCP server with DAG-based thought graph, multiple reasoning strategies, metacognition, and self-evaluation. A significant evolution beyond sequential-thinking MCP, providing structured deep reasoning with graph-based thought management.
    Last updated
    6
    59
    10
    MIT
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    Analyzes, refines, and optimizes prompts for AI assistants by fixing grammar, improving clarity, applying best practices like chain-of-thought and few-shot learning, and scoring prompt quality across multiple dimensions.
    Last updated
    3
    53
    MIT