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"scikit-learn" matching MCP servers:

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    An MCP server for agent-guided DSA practice that generates LeetCode-style problems and provides tutoring with escalating hints, concept explanations, and progress tracking.
    7
    7
    MIT
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    Enables AI agents to design RF filters and SMPS-EMC from spec using three MCP servers that drive LTspice, Qucs-S, and scikit-rf, with closed-form synthesis, real-component optimization, and CISPR-aware compliance checking.
    AGPL 3.0
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    Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.
    16
    7
    47
    Apache 2.0
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    A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
    10
    MIT
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    MCP server for persistent, compounding memory that automatically captures corrections and insights across AI sessions, enabling agents to learn and improve over time.
    5
    364
    MIT
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    Enables Claude to remember conversations and learn over time by storing and recalling messages, memory abstracts, and recent history using a local SQLite database.
    4
    25
    72
    MIT
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    A Model Context Protocol server that gives AI coding agents 16 tools to secure what they build: install the one-line Nemesis Shield SDK, learn/approve/enforce app protection, put a domain behind a Cloudflare-like edge, guard LLMs (OWASP LLM Top 10), and create + score Omniguard business-logic fraud rules. Runs locally over stdio; the discovery tools work with no account.
    16
    0
    MIT
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    Enables AI agents to learn from their sessions by inspecting history, proposing rules or skills, and automatically persisting them to configuration files.
    4
    MIT
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    Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.
    5
    MIT
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    Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.
    2
    MIT