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    Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.
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    MIT
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    Enables AI assistants to perform comprehensive code reviews of local uncommitted changes by combining git diffs with static analysis from linters like ESLint and TypeScript. Returns structured JSON feedback with findings, suggestions, and quality assessments powered by Google's Gemini CLI.
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    MIT
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    A comprehensive development toolkit with 23 tools covering code quality analysis, development efficiency, and project management. Enables AI-assisted code review, test generation, performance analysis, SQL generation, UI component creation, and automated project documentation.
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    MIT
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    Enables natural language interaction with the QIT CLI for testing WordPress/WooCommerce plugins. Supports running security, e2e, and activation tests, managing test environments, and accessing test results through conversational commands.
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    MIT
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    A production-minded MCP server that enables autonomous website design and management, with resources, safe Git operations, validation, Playwright inspection, Vercel previews, and JSONL audit logging. It also provides specialized audit agents for design, SEO, performance, accessibility, conversion, diagnostics, and landing pages.
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    Enables AI assistants to interact with Godot game projects through real-time error detection, automated testing, code analysis, and safe git-based patching. Provides comprehensive project context and development workflow automation for Godot developers.
    MIT
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    This MCP server builds a local flakiness knowledge graph from Playwright test run history and enables AI agents to query flaky tests, failure patterns, trends, and correlated git commits, helping diagnose test reliability without manual analysis.
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    MIT
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    Analyzes React component changes by performing structural analysis and generating visual diffs to identify pixel-level differences. It integrates with Figma to validate implementation compliance against design specifications and automates component reviews across git branches.
    MIT
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    Analyzes codebases to produce structured quality health reports with risk-scored modules based on git churn, test coverage, and test-to-source mapping, acting as an MCP server for AI coding agents and a standalone CLI.
    MIT
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    AI-powered codebase intelligence tool that builds a dependency graph of a git repo for structural Q&A and pre-PR code review. It exposes MCP tools for blast radius, review sessions, and static analysis, running locally over stdio with no LLM or network dependencies.
    MIT
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    Enables deterministic semantic verification of agent-generated SQL queries against a git-versioned contract, blocking incorrect queries and returning structured feedback for self-correction.
    MIT
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    Enables AI-powered, zero-trust code review with multiple models, supporting single files, git diffs, and multiple files, with security, performance, and architecture checks across 10+ languages.
    14
    MIT
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    A dual-track testing server that combines CLI test execution with Playwright-based browser testing and persistent SQLite logging. It enables automated test pipelines, Git integration, and evidence-based requirement generation to streamline the development lifecycle.