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    Local-first memory for coding agents. Discovers the memory, instructions, and rules Claude Code, Codex, and Cursor already wrote on your machine, combines them into one canonical Markdown tree, and serves grounded, cited recall through tools like ask_memory and canonical_memory.
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    MIT
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    Grounds gene-nomenclature work in the HUGO Gene Nomenclature Committee (HGNC) dataset, enabling resolution of gene symbols and IDs to canonical HGNC identifiers, plus cross-references and batch operations.
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    MIT
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    Unifies 11 protocol specification repositories (EIPs, BIPs, CIPs, etc.) and canonical contract addresses across 10 chains into a local FTS5 index, enabling coding agents to retrieve exact spec text and contract addresses via natural language queries.
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    MIT
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    Repository-native protocol and MCP server for coordinating work items, documentation, changelogs, and project memory between humans and AI agents, using Markdown files in a Git repository as the canonical data source.
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    MIT
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    Exposes the canonical WordCast knowledge surface — voice and TTS workflows, blog topics, FAQ, official links — to MCP-compatible AI clients. Read-only, no API keys required.
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    MIT
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    Scans MCP servers for prompt-injection, tool-poisoning, and SSRF vulnerabilities using 30+ canonical rules across 5 severity tiers, with optional signed safety reports for procurement.
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    MIT
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    Enables AI agents to manage a Canonical Landscape estate, including inventory, alerts, patching, and script execution, with built-in safety layers to prevent accidental destructive actions.
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    MIT
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    Enables LLMs to perform structured, verifiable knowledge operations using the Canonical Knowledge Structure (CKS) ecosystem, including validation, querying, comparison, evolution, and derivation of knowledge.
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    Exposes the canonical 17-0 knowledge surface including game rules, roster constraints, and entry points for the NFL roster strategy game to MCP-compatible AI clients.
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    MIT
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    An MCP server for validating, auto-fixing, and scaffolding TwinCAT 3 XML files using deterministic code quality tools and IEC 61131-3 OOP checks. It enables AI assistants to perform structural validation, apply safe fixes, and generate canonical code skeletons for industrial automation projects.
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    Enables monitoring Italian public funding opportunities, normalizing them into a canonical model, and ranking them against a company profile with a two-stage matcher.
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    MIT