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    Behavioral verification intelligence for AI coding agents. Reads a TypeScript or JavaScript repo, clusters functions into 25 semantic workflows (Authentication, Payments, Webhooks, Caching, Queue, and more), and emits concrete adversarial probes per workflow. 17 MCP tools, local SQLite state, zero cloud.
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    MIT
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    Generates realistic, referentially-coherent test data (SQL INSERTs, JSON, or CSV) from your database schema, resolving foreign keys and respecting constraints. Paste CREATE TABLE DDL or a JSON schema and get ready-to-run seed data with valid relationships.
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    MIT
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    Automatically scans project dependencies (npm, Composer) for security vulnerabilities using the OSV.dev database, providing real-time alerts and detailed remediation guidance directly in your IDE.
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    MIT
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    Turn any .NET test-automation solution into a queryable map in one SQLite file — features, steps, API clients, page objects, and their dependencies. Zero config, no AI, no network, 100% deterministic.
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    MIT
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    Local MCP server for querying VCD waveform files via SQLite; enables AI agents to ask precise questions about signal values, transitions, and clock cycles without dumping raw VCD text.
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    MIT
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    Enables local engineering workflow management by consolidating tickets, QA evidence, time tracking, root cause investigation, knowledge, and reporting into a single SQLite database, allowing generation of complete ticket packages for handoffs, dailies, or career evidence.
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    MIT
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    Generates realistic mock data using Faker.js for database seeding, API testing, and development environments. Supports person/company data, custom patterns, multi-locale generation, and structured datasets with referential integrity.
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    MIT
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    Provides comprehensive React Native development assistance with expert-level automated code remediation, security vulnerability fixes, performance optimization, and production-ready code generation. Includes testing suite generation, dependency management, and accessibility compliance tools.
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    MIT
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    BlueMouse is the "Prefrontal Cortex" for LLMs. It uses a 180k+ failure pattern database to validate code logic before execution, acting as a rigorous Quality Gate to prevent hallucinations and unsafe operations.
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    AGPL 3.0
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    MCP server for operating and testing Android devices with AI agents. Provides observation and action tools for interactive control and spreadsheet-driven test runs.
    Apache 2.0
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    Enables safe evaluation of trading-oriented MCP tools through a no-execution stdio mock with read-only signal listings, deterministic paper previews, and confirmation requests that always fail closed.
    Apache 2.0