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    Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.
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    Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.
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    Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".
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    MIT
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    coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content
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    Integrates Vale prose linting into AI coding assistants, enabling users to check text files for style and grammar issues using Vale's powerful linting engine. Provides automated style feedback with smart configuration discovery and rich formatted results.
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    MIT
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    Open Kioku is the local-first code intelligence and evidence layer for AI coding agents (Claude, Cursor, Codex, Zed, and other MCP clients). Instead of letting agents blindly crawl files, guess blast radius, or read the entire repository repeatedly, Open Kioku gives them a precise, pre-indexed code index to build evidence-backed plans before they edit, and verify boundary limits after they edit.
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    An MCP server that packs repositories into AI-friendly files using the Repomix tool, supporting local and remote repositories with size estimation and compression. It enables users to efficiently feed entire codebases into AI models while managing context window limits.
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    Mason indexes your codebase into a persistent concept map linking features and flows to their implementing files, so AI agents can answer "where is X implemented" without running grep/glob. It also provides pre-edit impact analysis and generates CLAUDE.md files from structured analysis of git history, architectural file sampling, and test mappings.
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    An MCP server that lets AI agents review code using language models, supporting git diffs, files, and snippets with severity levels. Works with Ollama (local) and hosted providers like OpenAI, Anthropic, and OpenRouter.
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    MIT