Autoresearch
Autonomous research loop inspired by karpathy/autoresearch — give it a plain-language goal; it infers a recommended contract, then grills each decision with you (goal…
Pacote de plugins · de Frad LEE
Resumo
Autoresearch é um pacote de plugins para ferramentas de programação com IA publicado por Frad LEE. Marketplaces que lemos e que o listam: 1 (Community GitHub marketplaces). A última mudança que registramos foi em 2026-10-20. Conforme nossa passagem pelos marketplaces em 2026-10-24.
Autonomous research loop inspired by karpathy/autoresearch — give it a plain-language goal; it infers a recommended contract, then grills each decision with you (goal measurability, artifact, evaluator, bounds) before iterating: cheap sequential rounds that escalate to a parallel GAN tournament when they plateau
O que pede
- Instala-se como pacote de plugins
- Hooks declarados: 1
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 fradser/dotclaude
/plugin install autoresearch@frad-dotclaude
Onde está publicado
| Marketplace | Status | Versão | Números | Verificado | Visto pela primeira vez |
|---|---|---|---|---|---|
| Marketplace da comunidade fradser/dotclaude (Abrir o código no GitHub) | Listado | 0.9.2 | Forks (GitHub): 51 Estrelas (GitHub): 596 |
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) |
|---|---|---|
| frad-dotclaude | fradser/dotclaude | 596 |
Como os números mudaram
- 2026-10-2051
- 2026-10-2451
- 2026-10-20596
- 2026-10-24596
Cada ponto é um dia em que lemos um número diferente; os trechos planos são dias em que o número não mudou. Números de marketplaces diferentes nunca são somados.
O que o pacote contém
Conforme publicado em Community GitHub marketplaces.
Comandos: 3
cancelhelpstart
Hooks: 1
Stop
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.
-
Apply Karpathy's autoresearch loop (goal + mechanical fitness + mutable surface + keep-or-revert iteration) to ANY measurable workflow - code, content, sales, research, design…
-
Autonomous experiment loops on any codebase — one file, one metric, one loop. An agent edits a constrained file, runs an eval, keeps improvements, reverts failures, and repeats…
-
Autonomous prompt optimization using Karpathy's autoresearch pattern. Editable file + fixed eval metric + autonomous improvement loop.
-
lazy-developer
Orchestrates autoresearch across a prioritized sequence of optimization goals — with configurable phase order and optional ralph-driven execution
-
Set up autonomous goal-driven optimization — creates a GOAL.md fitness function and runs an improvement loop
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Reviewable ML research: literature, planning, methodology review, bounded runs, sweeps, implementation verification, ablations, retrospectives, Kaggle, and setup
-
Autonomous experiment loop — modify, test, keep/discard, repeat forever
-
Drop-in agentic loop skills (autoresearch, literature & writing, data, optimization, red-team, power analysis). Skills install namespaced as agent-loops:<name>.
-
Autonomous SKILL.md improvement via Karpathy autoresearch-inspired iterative eval hill-climbing with force iteration, NEUTRAL mutation judgment, mutation exclusions, L1/L2/L3 eval…
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)
-
Autonomous experiment loops on any codebase — one file, one metric, one loop. An agent edits a constrained file, runs an eval, keeps improvements, reverts failures, and repeats…
-
Autonomous prompt optimization using Karpathy's autoresearch pattern. Editable file + fixed eval metric + autonomous improvement loop.
-
Autonomous skill improvement through parallel research agents. Inspired by Karpathy's autoresearch methodology, it spawns 5 parallel agents (domain expert, quality auditor…
-
Autonomous experiment loop for Claude Code. Run an endless optimize-measure-keep/discard cycle on any optimization target: LLM training loss, test speed, bundle size, build time…
-
Multi-agent deep research pipelines for Claude Code. Runs internet research, repository analysis, schema-driven structured research, and media research (via NotebookLM) using…
Listado em Claude Code: claude-plugins-official (5)
-
Interactive self-referential AI loops for iterative development, implementing the Ralph Wiggum technique. Claude works on the same task repeatedly, seeing its previous work, until…
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, and…
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Listado em 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…
-
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.
-
A suite of agent skills for the full GrowthBook feature flag and experimentation lifecycle.
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
Listado em 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…
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
An agentic skills framework & software development methodology that works: planning, TDD, debugging, and collaboration workflows.
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
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)
-
Apply Karpathy's autoresearch loop (goal + mechanical fitness + mutable surface + keep-or-revert iteration) to ANY measurable workflow - code, content, sales, research, design…
-
Autonomous experiment loops on any codebase — one file, one metric, one loop. An agent edits a constrained file, runs an eval, keeps improvements, reverts failures, and repeats…
-
lazy-developer
Orchestrates autoresearch across a prioritized sequence of optimization goals — with configurable phase order and optional ralph-driven execution
-
Set up autonomous goal-driven optimization — creates a GOAL.md fitness function and runs an improvement loop
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
Listado em Copilot CLI: github/awesome-copilot (5)
-
Goal-driven task orchestration with independent verification. Interviews the user to define a clear goal, then loops between a Builder subagent (does the work) and an Inspector…
-
Premium planning, thrifty implementation
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Record your screen doing a manual process, drop the video on your Desktop, and let Copilot CLI analyze it frame-by-frame to build working automation scripts. Supports narrated…
Listado em Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
Bring Azure DevOps work-item, repository, and pull-request workflows into GitHub Copilot.
-
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…
-
Advanced Security plugin for GitHub Copilot.
-
Complete bundle: all Microsoft Skills for Fabric for developers and consumers.
Listado em Cursor: cursor/plugins (5)
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Consult a stronger model before major decisions, when stuck, and before declaring done.
-
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.
-
Patterns for designing CLIs that coding agents can run reliably: flags, help with examples, pipelines, errors, idempotency, dry-run.
-
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.
-
Sequential Thinking (Reference)
Dynamic and reflective problem-solving through thought sequences.
-
Real-time web search, reasoning, and research through Perplexity's API
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
Tools for interacting with Hugging Face models, datasets, research papers, and more.
Listado em Official MCP Registry (5)
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
openpaper-mcp io.github.federicodeponte/openpaper-mcp
Autonomous research engine: auto research from a prompt to a fully-cited academic paper via MCP
-
forkmind io.github.Medhovarsh/forkmind
Branchable LLM history DAG + encrypted context capsules agents can save, restore, and crypto-shred.
-
agentic-research io.github.TheAiSingularity/agentic-research
Local research agent that verifies its own answers. Runs on Gemma 3 4B + Ollama, $0/query.
-
Create, launch, and analyze user research studies, AI interviews, and AI tests from your AI agent.
Listado em PyPI (MCP servers) (5)
-
Search Lacuna's ML research graph for papers, directions, authors, venues, and hypotheses.
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
hypermnesia-mcp io.github.cdeust/hypermnesia-mcp
Persistent memory for AI agents — 36 cited neuroscience mechanisms, local-first, hybrid retrieval.
-
Let one coding-agent CLI consult another: Codex, Claude Code, OpenCode, Copilot or Antigravity.
-
parallel com.mcparmory/parallel
Search the web, extract content, and run distributed tasks with AI-powered automation
Listado em npm registry (MCP servers) (5)
-
review-assist-mcp io.github.uditk2/review-assist-mcp
Turns an AI coding agent session into a reviewable Intent Document, validated against the diff.
-
Local MCP server for AI coding safety: risk checks, sessions, checkpoints, and fix prompts.
-
google-surf-mcp io.github.HarimxChoi/google-surf-mcp
Web, academic and code search with graph RAG, data lineage, ontology and cross-project schema links.
-
gitmem io.github.gitmem-dev/gitmem
Persistent institutional memory for AI coding agents. Memory that compounds.
-
Hands bounded coding work to cheaper worker agents: verified answers, exact diffs, cost receipts.
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.