Showing 4 open source projects for "diagnostics"

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    LazyCodex

    LazyCodex

    The one and only agent harness for complex codebases

    LazyCodex is an agent harness for using Codex on complex software projects. It is designed to add structure around AI coding sessions through memory, planning, execution, verification, skills, hooks, routing, and diagnostics. The project helps developers move beyond one-off prompts by giving the agent a more organized workflow inside a codebase. It supports project memory so context can persist across sessions and decisions do not need to be repeatedly reintroduced. LazyCodex also emphasizes verified completion, which means the workflow is built around checking whether tasks are actually finished rather than only generating code. ...
    Downloads: 1 This Week
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    React Doctor

    React Doctor

    Your agent writes bad React

    React Doctor is a developer tool that scans React codebases and identifies problems that commonly appear in AI-generated or poorly maintained frontend code. It gives projects a clear health score from 0 to 100, making technical issues easier to understand, prioritize, and communicate. The scanner checks areas such as state management, effects, performance, architecture, accessibility, security, and dead code. It works across popular React environments, including Next.js, Vite, and React...
    Downloads: 0 This Week
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  • 3
    OpenTag

    OpenTag

    Open-source @agent mentions for Slack and GitHub

    ...A local work ledger records the source event, admission decision, context, capabilities, artifacts, callbacks, and final outcome. The CLI supports guided setup, diagnostics, terminal operation, and background services on macOS and Linux.
    Downloads: 0 This Week
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  • 4
    Responsible AI Toolbox

    Responsible AI Toolbox

    Responsible AI Toolbox is a suite of tools providing model

    ...It is designed to integrate with common machine learning frameworks, especially PyTorch, allowing developers to apply responsible AI techniques within existing workflows. The toolbox includes methods for adversarial testing, interpretability analysis, and model diagnostics that help developers understand how models behave under different conditions. These capabilities are particularly important for high-impact domains where AI systems must meet strict reliability and fairness requirements. By offering reusable components and standardized workflows, the framework helps organizations implement responsible AI practices throughout the machine learning lifecycle.
    Downloads: 0 This Week
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