training
Corpus-to-dataset pipeline for AI training data curation. Ingests sources, synthesizes examples, generates DPO/KTO preference pairs, applies decontamination, and exports to…
Pacote de plugins · de jmagly
Resumo
training é um pacote de plugins para ferramentas de programação com IA publicado por jmagly. Marketplaces que lemos e que o listam: 1 (Community GitHub marketplaces). Vimos pela primeira vez em 2026-10-23 e não registramos mudanças desde então. Conforme nossa passagem pelos marketplaces em 2026-10-27.
Corpus-to-dataset pipeline for AI training data curation. Ingests sources, synthesizes examples, generates DPO/KTO preference pairs, applies decontamination, and exports to Alpaca/ShareGPT/ChatML/JSONL/Parquet with provenance. Optional Python runtime (aiwg-training CLI) for scale.
O que pede
- Instala-se como pacote de plugins
Nenhum dos marketplaces que lemos publica as ferramentas ou as configurações que ele pede.
O que declara, pede e executa, por completo
Instale apenas plugins de desenvolvedores em quem você confia. Mostramos o que os marketplaces publicam e não testamos plugins.
Funciona com
- Claude Code
Pelos marketplaces que o listam e pelo transporte MCP que declara, conforme cada cliente documenta. Não testamos plugins.
Conectar
Comandos e configurações gerados apenas a partir do que os marketplaces declaram. Não os executamos por você e não testamos plugins.
Como plugin do Claude Code
Gerado a partir do que Community GitHub marketplaces declara.
/plugin marketplace add jmagly/aiwg
/plugin install training@aiwg
Onde está publicado
| Marketplace | Status | Versão | Números | Verificado | Visto pela primeira vez |
|---|---|---|---|---|---|
| Marketplace da comunidade jmagly/aiwg (Abrir o código no GitHub) | Listado | 1.0.0 | Forks (GitHub): 1 Estrelas (GitHub): 1 |
Sem marcação |
Cada número é do próprio marketplace e nunca é somado entre marketplaces. Um marketplace que não publica números não mostra nenhum: sem número não é zero.
Listado em um marketplace da comunidade
| Marketplace | Repositório | Estrelas (GitHub) |
|---|---|---|
| aiwg | jmagly/aiwg | 225 |
O que o pacote contém
Conforme publicado em Community GitHub marketplaces.
Skills: 15
acquire-training-sourcedataset-docsdataset-reproducedataset-versiondecontamination-checkexample-quality-assessexample-synthesizerflow-dataset-buildformat-adapter-alpacaformat-adapter-chatmlformat-adapter-jsonlformat-adapter-parquet
Mostrar mais 3
format-adapter-sharegptpreference-generatorsynthetic-data-generator
Agentes: 7
dataset-evaluator-agentdataset-publication-agentdecontamination-agentexample-synthesizer-agentformat-converter-agentpreference-generator-agentsource-curator-agent
Regras: 2
decontamination-gatelicense-check
O que mudou
- Passou a ser um produto que acompanhamos
- Community GitHub marketplaces: apareceu
Plugins semelhantes
Produtos cujos próprios nomes, descrições, ferramentas e skills são os mais próximos deste pelo significado, em todos os marketplaces que lemos. A proximidade é ordenada apenas dentro desta lista.
-
Create evaluation datasets in JSON, CSV, or JSONL format for Dokimos
-
Turn any document into a production-ready RAG dataset from Claude, Cursor, or any MCP host.
-
Search and load AI datasets, prompts and few-shot contexts from the ContexteTech catalogue.
-
Search the Mozilla Data Collective catalog of ethically sourced AI training datasets.
-
Validate and convert JSONL fine-tuning data across 11 AI providers. 13 tools.
-
End-to-end data annotation toolkit. Prep raw data, design annotation schemas, annotate interactively with Claude (small scale) or scaffold Gemini batch inference (large scale…
-
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
-
AI/ML skills including prompt engineering, RAG, and chunking strategies
-
Search and license real-world AI training data: egocentric video, UMI gripper demos, custom EEG.
-
golden-dataset-mcp io.github.nipunkhanderia/golden-dataset-mcp
Version-controlled golden datasets and RAG evaluation, no API key needed.
Plugins semelhantes por marketplace
A mesma pergunta feita a cada marketplace separadamente: existe lá um equivalente deste plugin?
Listado em Claude Code: claude-plugins-community (5)
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
-
Honeydew AI plugins for semantic data modeling and data analysis — 9 skills, 3 hooks, multi-platform support.
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
End-to-end data analysis workflow plugin for Claude Code. Covers the full pipeline from EDA to model training - EDA, preprocessing, statistical analysis, feature engineering, and…
-
Output.ai workflow development toolkit for Claude Code. Adds 5 specialist agents (planner, builder, debugger, prompt writer, quality reviewer), 40+ slash-command skills covering…
Listado em Claude Code: claude-plugins-official (5)
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
Output.ai workflow development toolkit for Claude Code. Adds 5 specialist agents (planner, builder, debugger, prompt writer, quality reviewer), 40+ slash-command skills covering…
-
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.
Listado em Claude: knowledge-work-plugins (5)
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
Atlan data catalog plugin for Claude Code. Search, explore, govern, and manage your data assets through natural language. Powered by the Atlan MCP server with semantic search…
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
Listado em Codex: openai-curated (openai/plugins) (5)
-
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.
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
Answer product and business questions with data
-
CrowdStrike Falcon Foundry development skills for building cybersecurity applications on the Falcon platform. Includes UI development, collections, functions, workflows, API…
Listado em Community GitHub marketplaces (5)
-
Create evaluation datasets in JSON, CSV, or JSONL format for Dokimos
-
End-to-end data annotation toolkit. Prep raw data, design annotation schemas, annotate interactively with Claude (small scale) or scaffold Gemini batch inference (large scale…
-
AI/ML skills including prompt engineering, RAG, and chunking strategies
-
Plan small custom models and fine-tunes: check the Hub, research options, write the spec in a planning repo.
-
Agents for data engineering, machine learning, and AI development
Listado em 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.
-
Three-layer verification pipeline for AI output. Extracts claims, finds sources, and flags hallucination risks so humans can verify before acting.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
A runtime-portable 5-agent quality gate that adjudicates text artifacts before they ship. Five role-conditioned LLM deliberators run a 2-round async protocol with cross-read…
-
Microsoft Copilot Studio YAML authoring toolkit. Create, edit, validate, and test Copilot Studio agents using YAML files.
Listado em Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
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…
-
workiq
Query Microsoft 365 data with natural language — emails, meetings, documents, Teams messages, and more.
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
-
Microsoft Dataverse plugin for coding agents, covering record CRUD, bulk data operations, advanced queries, schema and metadata, administration, security, environment management…
Listado em Cursor: cursor/plugins (5)
-
Incremental transcript-driven memory updates for AGENTS.md using high-signal bullet points only.
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Ask Databricks Genie questions about your workspace's data in plain language.
-
Generate presentations, documents, and webpages.
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
Listado em Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Analyze documents, images, videos, and audio.
-
Tools for interacting with Hugging Face models, datasets, research papers, and more.
-
Production-ready RAG service to search and retrieve data from your documents.
-
An MCP server to help make U.S. Government open datasets AI-friendly.
Listado em Official MCP Registry (5)
-
Turn any document into a production-ready RAG dataset from Claude, Cursor, or any MCP host.
-
Search and load AI datasets, prompts and few-shot contexts from the ContexteTech catalogue.
-
Search the Mozilla Data Collective catalog of ethically sourced AI training datasets.
-
Validate and convert JSONL fine-tuning data across 11 AI providers. 13 tools.
-
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
Listado em PyPI (MCP servers) (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
local-delegate io.github.ZahiriNatZuke/local-delegate
Delegate mechanical text tasks to a local OpenAI-compatible LLM to save Claude quota.
-
Find & fetch research datasets across 21 archives, omics registries, and literature sources.
-
marm-mcp-server io.github.Lyellr88/marm-mcp-server
Universal MCP Server with advanced AI memory capabilities and semantic search.
-
perplexity com.mcparmory/perplexity
Search the web, generate AI responses, and create embeddings with real-time information
Listado em npm registry (MCP servers) (5)
-
Compile documents into a cited knowledge wiki. Retrieve evidence, query, and review via local MCP.
-
dossier ai.imboard/dossier
MCP server for dossier automation standard - enables LLMs to discover, verify, and execute dossiers
-
Local-first memory for AI coding agents — Markdown + SQLite FTS5, shared across editors via MCP.
-
rubber-duck io.github.nesquikm/rubber-duck
Bridge to multiple LLMs and CLI agents: councils, debates, voting, and more
-
One workspace for every AI coding assistant. Shared tasks, memory, and governance.
Encontrados pelo significado, não por código ou repositório em comum: plugins semelhantes continuam sendo produtos distintos, e isto não é uma avaliação nem uma recomendação.
Quer esta marca no seu plugin em nosso catálogo?
Prove que o mantém com um arquivo no repositório, um arquivo no seu domínio ou um registro DNS, e você poderá mostrar esta marca na ficha, com banners ao vivo para o seu site e o README. Como funciona a verificação
Você também recebe alertas quando um marketplace o publica, o remove, muda a marca de verificado ou o que ele pede, e o endpoint remoto passa a ser monitorado como um serviço web.
Dados de Community GitHub marketplaces. Os números vêm das nossas próprias passagens regulares pelos marketplaces, última leitura em . Não publicamos dados de contato dos editores de plugins.