Search Results for "tool test"
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Showing 13 open source projects for "tool test"
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XcodeBuildMCP
A Model Context Protocol (MCP) server
XcodeBuildMCP is a Model Context Protocol server that exposes Xcode operations as typed tools and resources so AI assistants can build, test, and debug iOS apps programmatically. It’s organized with a modern plugin architecture and workflow-scoped tool directories, covering common developer actions across projects, schemes, targets, simulators, real devices, and Swift packages. The server aims to be “agent-ready,” surfacing capabilities (build, clean, test, archive, install, run, log collection) with explicit schemas instead of brittle prompt instructions. ... -
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Qodo Cover
AI tool that generates tests to improve code coverage quickly
Qodo Cover is an open source developer tool designed to automate the creation of unit tests using generative AI, helping teams improve code coverage with minimal manual effort. It operates as a command-line interface and can also be integrated into continuous integration workflows, making it adaptable to different development environments. It analyzes an existing codebase, identifies gaps in test coverage, and generates new tests that target uncovered or weakly tested areas. ... -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation... -
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Transformer Debugger
Tool for exploring and debugging transformer model behaviors
Transformer Debugger (TDB) is a research tool developed by OpenAI’s Superalignment team to investigate and interpret the behaviors of small language models. It combines automated interpretability methods with sparse autoencoders, enabling researchers to analyze how specific neurons, attention heads, and latent features contribute to a model’s outputs. TDB allows users to intervene directly in the forward pass of a model and observe how such interventions change predictions, making it... -
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Goose
AI coding agent that's more than suggestions - install, execute, edit+
Goose is an open-source, extensible AI agent that enhances the software development process by going beyond traditional code suggestions. It allows developers to install, execute, edit, and test code with any large language model (LLM), facilitating a more efficient and streamlined workflow. Designed to operate locally within a developer's environment, Goose integrates seamlessly with various tools and platforms, providing a customizable and powerful assistant for coding tasks. Its... -
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Expect
Let agents test your code in a real browser
Expect is a developer-focused utility designed to simplify validation, testing, and assertion workflows across software environments by providing a clean and expressive interface for defining expected outcomes. The project likely centers on improving readability and maintainability in testing scenarios, allowing developers to write expectations in a concise and human-readable format. It may support chaining conditions, enabling complex validation logic without introducing unnecessary... -
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CodeBurn
See where your AI coding tokens go
CodeBurn is a security-focused tool designed to evaluate and stress-test codebases using adversarial techniques, often leveraging AI to identify vulnerabilities and weaknesses. It simulates attack scenarios against code to uncover potential security risks, helping developers proactively identify issues before they reach production. The system is designed to integrate into development workflows, allowing continuous testing as code evolves. -
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Deta Surf
Personal AI Notebooks. Organize files & webpages and generate notes
Surf is an open-source AI-driven development tool designed to simplify the process of building and experimenting with artificial intelligence applications. The platform provides a streamlined development environment where developers can test models, run experiments, and deploy small AI services with minimal infrastructure overhead. It focuses on simplicity and speed, allowing developers to prototype ideas quickly without managing complex cloud configurations. -
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Claude Code Subagents Command Collection
Claude Code Subagents & Commands Collection + CLI Tool
This repository aggregates a large set of specialized subagents and slash commands designed for Claude Code, giving developers domain-focused “teammates” they can summon on demand. Each subagent is defined by a concise role, tools, and behaviors, and ships as Markdown you can drop into your .claude/agents/ directory. The collection targets common developer workflows such as scaffolding, refactoring, test writing, documentation, security checks, and project management. It includes a CLI... -
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Micro Agent
AI CLI agent that writes code by iterating until tests pass
Micro Agent is a command-line tool designed to generate and refine code using a test-driven approach powered by large language models. Instead of producing one-shot code outputs, it creates or uses test cases and repeatedly iterates on the generated code until those tests pass successfully. This workflow emphasizes reliability by using structured feedback from failing tests to guide improvements, reducing the need for manual debugging and iteration. -
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Arthur Bench
Bench is a tool for evaluating LLMs for production use cases
Bench is a tool for evaluating LLMs for production use cases. Whether you are comparing different LLMs, considering different prompts, or testing generation hyperparameters like temperature and # tokens, Bench provides one touch point for all your LLM performance evaluation. -
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ZeroStep
Supercharge your Playwright tests with AI
ZeroStep is a tool that enhances Playwright tests with AI capabilities, aiming to improve the efficiency and effectiveness of end-to-end testing. -
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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income...
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