sklearn-to-ts
Train sklearn models (MLP, Random Forest, Linear Regression), export weights to JSON, and generate pure TypeScript inference code with zero npm dependencies. Handles the full…
Пакет плагинов · от TGKMeissner
Коротко
sklearn-to-ts: пакет плагинов для ИИ-инструментов разработки, издатель TGKMeissner. Витрин, которые мы читаем и где он опубликован: 1 (Claude Code: claude-plugins-community). Мы впервые увидели его 2026-10-12 и с тех пор изменений не фиксировали. Данные нашего прохода по витринам на 2026-10-12.
Train sklearn models (MLP, Random Forest, Linear Regression), export weights to JSON, and generate pure TypeScript inference code with zero npm dependencies. Handles the full pipeline: feature engineering, synthetic data generation, model training, weight export, and TypeScript codegen. The resulting model runs server-side (Node.js/Next.js) or client-side (browser) in <1ms with a ~10-50 KB weights file. No Python runtime, no ONNX, no TensorFlow.js needed at runtime.
Что просит
- Ставится как пакет плагинов
Ни одна из витрин, которые мы читаем, не публикует его инструменты или настройки, которые он просит.
Что объявляет, просит и запускает, полностью
Устанавливайте плагины только от разработчиков, которым доверяете. Мы показываем то, что публикуют витрины, и не проверяем плагины.
Работает с
- Claude Code
По маркетплейсам, где он опубликован, и объявленному транспорту MCP, как их документирует каждый клиент. Плагины мы не тестируем.
Подключить
Нет данных для автоматической команды: из того, что объявляют витрины, нельзя однозначно понять, как его подключить.
Посмотреть карточку на Claude Code: claude-plugins-community
Где опубликован
| Витрина | Статус | Версия | Числа | Проверен | Впервые замечен |
|---|---|---|---|---|---|
| Claude Code: claude-plugins-community (Открыть исходники на GitHub) | Опубликован | 1.0.0 | Форки (GitHub): 0 Звёзды (GitHub): 0 |
Не помечен |
Каждое число принадлежит своей витрине и никогда не складывается с числами других витрин. Если витрина число не публикует, мы его не показываем: нет числа не значит ноль.
Что входит в пакет
Так опубликовано на витрине Claude Code: claude-plugins-community.
Скиллы: 1
sklearn-to-ts
Что менялось
С тех пор как мы следим за плагином, ничего не менялось.
Похожие плагины
Продукты, чьи собственные названия, описания, инструменты и скиллы ближе всего по смыслу к этому, на всех витринах, которые мы читаем. Близость упорядочена только внутри этого списка.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
Forecast time series in CSV files: deterministic skforecast workflows and the script that ran.
-
DataRobot Agent Skills - Agentic skills for DataRobot enterprise AI and ML workflows. Covers model training, deployment, predictions, feature engineering, monitoring…
-
Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
-
Skills to support Machine Learning experimentation using the Python ecosystem.
-
together-ai-skills
A collection of skills that teach coding agents how to use the Together AI platform, including chat completions, image generation, video, audio, embeddings, fine-tuning, batch…
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
Signed-in OneInfer models, chat, media, Studio movies, music, GPUs, and credits.
Похожие плагины по витринам
Тот же вопрос к каждой витрине по отдельности: есть ли там аналог этого плагина?
Есть на витрине Claude Code: claude-plugins-community (5)
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
DataRobot Agent Skills - Agentic skills for DataRobot enterprise AI and ML workflows. Covers model training, deployment, predictions, feature engineering, monitoring…
-
Skills to support Machine Learning experimentation using the Python ecosystem.
-
together-ai-skills
A collection of skills that teach coding agents how to use the Together AI platform, including chat completions, image generation, video, audio, embeddings, fine-tuning, batch…
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
Есть на витрине Claude Code: claude-plugins-official (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Есть на витрине Claude: knowledge-work-plugins (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
Agent skills for Qdrant vector search covering scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across…
Есть на витрине Codex: openai-curated (openai/plugins) (5)
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
Design, critique, implement, test, and export web data visualizations in Codex, including charts, maps, dashboards, Gantt, UML, scrollytelling, reports, slides, mobile views…
-
Answer product and business questions with data
Есть на витрине Community GitHub marketplaces (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
-
Endor Labs workflow agents and setup for Claude Code.
-
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless…
-
Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, and…
Есть на витрине Copilot CLI: github/awesome-copilot (5)
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
-
Model, visualize, and deploy applications with Radius from the GitHub Copilot app and CLI.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Build applications with the GitHub Copilot SDK across multiple programming languages. Includes comprehensive instructions for C#, Go, Node.js/TypeScript, and Python to help you…
-
Create, edit, run, save, and checkpoint Jupyter notebooks in an interactive Copilot canvas.
Есть на витрине Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
mobile-app
Build and deploy Power Apps code apps for mobile using Expo (React Native), with native device functionality and Power Platform connectors
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
-
code-apps-preview
Build and deploy Power Apps code apps using React, Vite, and Power Platform connectors.
-
model-apps
Build and deploy Power Apps generative pages for model-driven apps with specialist agents for planning, entity creation, and parallel code generation. Requires PAC CLI >= 2.7.0…
Есть на витрине Cursor: cursor/plugins (5)
-
Build apps, scripts, and automations with the TypeScript SDK.
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Skills for building on the Cursor Origin API. Fetch the live spec first, then follow the Origin rules for credentials, scopes, webhooks, paging, and errors. Includes a skill that…
-
if you want to go fast, go deep first. pstack helps you write less, but higher quality code. rigorous agent workflows you can parallelize with confidence.
Есть на витрине Docker MCP Catalog (5)
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Giving Claude ability to run code with E2B via MCP (Model Context Protocol).
-
Tools and resources for writing Effect code in Typescript.
-
Seamless interaction with Novita AI platform resources.
Есть на витрине Official MCP Registry (5)
-
Forecast time series in CSV files: deterministic skforecast workflows and the script that ran.
-
Signed-in OneInfer models, chat, media, Studio movies, music, GPUs, and credits.
-
Give your AI a research team. Forecast, score, classify, or research every row of a dataset.
-
Self-hosted MCP server for time-series forecasting and tabular ML via foundation models.
-
Mini Data Engine Runtime Copilot
MCP runtime copilot for health checks, regressions, explainability, and operational memory.
Найдено по смыслу, а не по общему коду или репозиторию: похожие плагины остаются разными продуктами, и это не обзор и не рекомендация.
Это ваш плагин?
Добавьте плагин в свои проекты и докажите, что поддерживаете его: файлом в репозитории, файлом на вашем домене или DNS-записью. Вы будете получать уведомления, когда витрина добавит плагин, снимет его, изменит отметку verified или список того, что он просит, а его удалённый endpoint встанет на мониторинг как веб-сервис.
Данные витрины Claude Code: claude-plugins-community. Числа взяты из наших регулярных обходов витрин, последняя проверка . Контакты издателей плагинов мы не публикуем.