Search Results for "claw-code" - Page 30
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Showing 2671 open source projects for "claw-code"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Raven
Self-evolving multi-agent ecosystem for all-domain collaboration
...It ships with built-in agents for research, coding, visual design, and supervision of long-running jobs. Raven can also orchestrate third-party agents such as Claude Code, Codex, OpenCode, GitHub Copilot, Pi, and others. Its runtime is powered by EverOS, allowing user context, agent experience, and reusable knowledge to persist across sessions. The system can generate task graphs, delegate work, combine results, and evolve tools, skills, and workflows over time. Raven supports a terminal interface, web UI, plugins, provider routing, and self-hosted deployment. ... -
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AI Copywriter
An AI copywriter that uses real copywriting skills + real marketing
...It refuses to invent product facts or unsupported numbers and requests stronger evidence when a claim needs proof. Because the core artifact is a single SKILL.md file, it can run in Claude Code, ChatGPT, Manus, and other instruction-compatible agent systems. -
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gzh-design-skill
An AI-agent skill that turns Markdown into paste-ready WeChat article
...It uses fully inline styling and platform-aware markup so pasted articles retain their formatting after WeChat filters the content. The skill recognizes headings, quotations, lists, code blocks, images, highlights, and other Markdown structures before assembling them from reusable components. Six built-in themes cover tutorials, reviews, essays, reports, technology writing, and editorial-style articles. A theme generator can create additional component libraries from a written design direction or reference image. ... -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
...The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts. -
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DeepSeek Engineer v2
A powerful coding assistant application
DeepSeek Engineer v2 is an AI-powered coding assistant built around DeepSeek models and an interactive terminal workflow. It lets developers discuss code, request analysis, and perform project work through natural language. Version 2.0 focuses on native function calling instead of rigid structured JSON responses. The assistant can read files, read multiple files, create files, create multiple files, and edit specific snippets when needed. It includes safeguards such as path validation, directory traversal protection, file size limits, and binary file exclusion. ... -
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kagglehub
Python library to access Kaggle resources
kagglehub is a Python library for accessing Kaggle resources directly from Python code. It provides a simple API for downloading datasets, models, competition files, and notebook outputs without requiring users to manually manage every URL or file path. The library is designed to work both inside and outside Kaggle Notebooks, with native behavior that can adapt when it runs in Kaggle’s hosted notebook environment. It is useful for machine learning workflows where data, models, and notebook artifacts need to be pulled into scripts, experiments, or pipelines. kagglehub also supports authentication so users can access private or restricted resources when their account has permission. ... -
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MiniMind-O
A 0.1B Omni model trained from scratch
...It includes both mini and full training data paths, allowing learners to run a complete workflow quickly or reproduce the released model setup more closely. The implementation emphasizes native PyTorch code instead of relying on high-level third-party abstractions. minimind-o is most useful for developers and researchers who want to understand how multimodal and speech-capable AI systems are built from the ground up. -
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Data Contract CLI
Enforce Data Contracts
...It supports both the Data Contract Specification and the Open Data Contract Standard, making it useful for teams standardizing data governance across different platforms. It can run locally, inside CI/CD pipelines, through Docker, or directly from Python code. Overall, it helps data producers and consumers treat data products more like APIs, with explicit expectations, automated checks, and clearer accountability. -
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GPTImage2Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
...Its gallery is organized into category files so an agent can load only the relevant prompt references instead of overwhelming the context window. It also includes installation paths for skill-capable environments such as Claude Code, Codex, OpenClaw, and other agent runtimes. Overall, it is useful as both a learning resource for prompt structure and a practical toolkit for repeatable image generation workflows. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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adversarial-spec
A Claude Code plugin that iteratively refines product specifications
adversarial-spec is a framework focused on designing and testing systems using adversarial thinking to uncover weaknesses and improve robustness. It encourages developers to define specifications that anticipate failure modes, edge cases, and malicious inputs before implementing solutions. The project emphasizes proactive design, ensuring that systems are built with resilience in mind from the beginning. It provides structured approaches for identifying vulnerabilities and stress-testing... -
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My Python Eggs
Python Examples
My Python Eggs, commonly associated with the geekcomputers Python repository, is a large collection of practical Python scripts and small programs created primarily for experimentation, automation, and educational purposes. Rather than being a single cohesive application, it functions as a repository of utilities that demonstrate how Python can be used to solve everyday problems and automate repetitive tasks. The scripts cover a wide range of topics, including file management, networking,... -
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fireworks-tech-graph
Claude Code skill for generating production-quality SVG+PNG technical
fireworks-tech-graph is an AI-driven project focused on building structured knowledge graphs that map relationships between technologies, concepts, and entities within technical domains. It aims to transform unstructured information into interconnected graphs that can be queried and analyzed for insights, making it easier to understand complex ecosystems such as software stacks or research fields. The system likely leverages AI techniques for entity extraction, relationship mapping, and... -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
Writer Framework is an open source platform designed to help developers build AI-powered applications by combining a visual interface builder with a Python-based backend architecture. It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large... -
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SEO Machine
A specialized Claude Code workspace for creating long-form
SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It... -
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ZML
Any model. Any hardware. Zero compromise
...The system allows models to be compiled and executed across multiple types of accelerators, including GPUs and TPUs, even when distributed across different machines or locations. One of its key strengths is cross-compilation, enabling developers to build once and deploy across various platforms without rewriting code. zml provides example implementations of models and workflows, demonstrating how to run inference tasks such as image classification or large language models. It is designed to handle complex distributed setups, including scenarios where model components are split across devices connected via networks. -
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Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
Google Kubernetes Engine (GKE) Samples repository is a comprehensive collection of sample applications and reference implementations designed to demonstrate how to build, deploy, and manage workloads on Google Kubernetes Engine (GKE). It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters. The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. ... -
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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 of workflows, PySpur makes it easier to debug interactions between components and identify failures in complex pipelines. ... -
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
...It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. The repository provides code for training models, performing inference on handwritten text images, and evaluating recognition accuracy. SimpleHTR is commonly used as an educational example for understanding how modern handwriting recognition systems operate. -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
how-to-optim-algorithm-in-cuda is an open educational repository focused on teaching developers how to optimize algorithms for high-performance execution on GPUs using CUDA. The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations such as reductions, element-wise computations, softmax, and attention mechanisms. ... -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer... -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
micrograd is a tiny, educational automatic differentiation engine focused on scalar values, built to show how backpropagation works end to end with minimal code. It constructs a dynamic computation graph as you perform math operations and then computes gradients by walking that graph backward, making it an approachable “from scratch” autograd reference. On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple models with a PyTorch-like feel, including multilayer perceptrons. ... -
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Hugging Face Skills
Definitions for AI/ML tasks like dataset creation
...Each skill is a self-contained folder with structured metadata and guidance that tells an agent how to execute tasks such as dataset creation, model training, evaluation, or Hub operations. The project is designed to be interoperable across major agent ecosystems, including Claude Code, OpenAI Codex, Gemini CLI, and Cursor, making it a cross-platform building block for agent automation. By formalizing best practices and workflows, Skills helps transform general-purpose coding agents into domain-aware assistants that can execute complex ML pipelines with less manual prompting. The repository also includes ready-to-use skills for common Hugging Face operations and encourages teams to extend them with custom domain logic. -
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papermill
Parameterize, execute, and analyze notebooks
...This capability is particularly useful in data science and analytics, where a template notebook might be reused for batching reports across dates, customers, or other variables without rewriting code or duplicating notebooks. Papermill supports both Python API usage and a command-line interface, making it flexible for integration with CI/CD systems, shells, and workflow orchestration tools like Airflow. -
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Trellis AI
All-in-one AI framework & toolkit for Claude Code & Cursor
Trellis is an advanced workflow and agent orchestration framework designed for building, managing, and scaling intelligent applications that coordinate numerous autonomous components. At its core, Trellis lets developers define units of work — called tasks or agents — and compose them into rich workflows that can operate with concurrency, conditional logic, and dynamic branching, all without sacrificing readability or control. It emphasizes modular design, encouraging users to encapsulate... -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
...It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. The framework also supports plugin extensions that add things like rate limiting, caching layers, and telemetry without cluttering core code.
