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    Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.
    5
    32 npm
    6
    Functional Source , Version 1.1, ALv2 Future
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    Exposes the four Ejentum cognitive harnesses (reasoning, code, anti-deception, memory) as MCP tools any agentic client can call. Drop-in scaffolding that catches LLM failure modes like sycophancy, hallucination, and reasoning shortcuts.
    4
    78 npm
    16
    MIT
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    A local MCP server that lets Claude Code read Ringg AI assistants, knowledge bases, and call history, and make targeted edits to prompts, custom variables, and knowledge base attachments without placing calls.
    10
    MIT
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    Enables production-grade answer verification for LLM agents by independently re-checking answers with a configurable verifier model, enforcing confidence policies, and producing Ed25519-signed, auditable verification results with optional web search and knowledge-base evidence.
    3
    MIT
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    Enables interaction with Google Ads accounts through the Model Context Protocol, specifically tuned for local service businesses. Provides account health scoring, search term waste analysis, and diagnostic insights without requiring GAQL knowledge.
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    107 npm
    1
    MIT
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    Provides AI agents with structured knowledge about 67+ generative AI models, including model recommendations, prompt formatting, parameter guidance, and validation. It acts as a prompt engineering co-pilot that helps agents use their existing tools more effectively.
    12
    76 npm
    3
    MIT
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    MCP server that provides a shared team knowledge base by transforming raw input into anonymous, factual knowledge via a local LLM before storage, enabling cross-session recall of team decisions and corrections.
    8
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    MIT