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    LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.
    10
    MIT
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    maintenance
    Enables AI assistants to interact with DataDog's observability platform through a standardized interface. Supports monitoring infrastructure, managing events, analyzing logs and metrics, and automating operations like alerts and downtimes.
    1
    MIT
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    Enables querying Prometheus metrics, performing cluster diagnostics, and analyzing resource usage through natural language, with automatic Kubernetes connectivity.
    MIT
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    CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.
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    MIT
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    MCP server for a self-hostable personal planner — exposes memo, todo, calendar event, and cross-resource full-text search tools so Claude, Cursor, or any MCP client can manage your schedule and notes in natural language.
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    1
    MIT
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    Self-hostable, markdown-native team wiki with a built-in MCP server: agents search, read, and write your wiki pages (ranked Postgres full-text + semantic search, backlink traversal). Plus Atlas, which auto-generates a cited, coverage-checked wiki from your git repos and Jira.
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    AGPL 3.0
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    Local-first Git hosting for AI coding agents. lgh serve turns any directory into an HTTP Git remote (127.0.0.1:9418) with push events, commit-status reporting, smart .gitignore generation, secret/large-file detection, and optional LAN sharing with auth. The MCP server exposes 10 tools covering repo registration, one-command backup (lgh up), server control, and event access.
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    MIT
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    A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.
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    MIT
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    A deterministic symbol oracle over a locally built index of the repository: whether a symbol exists, what a file declares, and what changed structurally since the last snapshot. It also ships a PreToolUse guard that stops an Edit/Write referencing a function, constant or type the repo does not declare, before the write lands rather than after the build fails. No LLM, no API keys, no network, no l
    6
    MIT
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    Local ledger for development ports: leases a block of ports per project slot (one per working copy), renders env files, and resolves service names to URLs. No daemon, no listener.
    6
    MIT
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    MCP server that indexes Markdown, Word, HTML, and PDF documents into a SQLite knowledge graph with CJK+Latin full-text search and cross-document reference tracking. Runs drift audits to surface stale policies, conflicting research claims, superseded ADRs, and undocumented code exports.
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    8
    MIT
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    quality
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    maintenance
    Portable memory protocol for AI coding sessions. Push the state of a session (goal, decisions, attempts, next step) from one MCP-capable agent and pull it back from another.
    12
    8
    Apache 2.0
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    license
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    quality
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    maintenance
    Enables AI to search, filter, and process CNKI academic literature through natural language, including structured search, metadata retrieval, full-text reading, citation export, and PDF/CAJ download.
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    GPL 3.0
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    MCP server for patent search and prior art discovery powered by Google Patents public dataset on BigQuery. Supports searching patents, fetching full patent details with CPC codes and citations, and retrieving legal claims text.
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    MIT
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    One guarded pass for all your coding agents. A derbent was a guarded post on an Turkish History mountain pass: its keepers decided who went through and kept a record of everyone who did. Derbent does the same for your coding agents' tool calls.
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    1
    Apache 2.0
  • A
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    maintenance
    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