autoresearch-agent
Autonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/ar:setup, /ar:run, /ar:loop, /ar:ar-status, /ar:ar-resume), 8 built-in evaluators…
Lot de plugins · par Alireza Rezvani
Aperçu
autoresearch-agent est un lot de plugins pour les outils de code IA publié par Alireza Rezvani. Places de marché que nous lisons et qui le référencent : 1 (Community GitHub marketplaces). Nous l'avons vu pour la première fois le 2026-10-14 et n'avons enregistré aucun changement depuis. Selon notre passage sur les places de marché du 2026-10-15.
Autonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/ar:setup, /ar:run, /ar:loop, /ar:ar-status, /ar:ar-resume), 8 built-in evaluators, configurable loop intervals (10min to monthly).
Ce qu'il demande
- S'installe comme lot de plugins
Aucune des places de marché que nous lisons ne publie ses outils ni les réglages qu'il demande.
Ce qu'il déclare, demande et exécute, en détail
N’installez que des plugins de développeurs en qui vous avez confiance. Nous reprenons ce que publient les places de marché et ne testons pas les plugins.
Fonctionne avec
- Claude Code
D'après les marketplaces qui le listent et le transport MCP qu'il déclare, tels que chaque client les documente. Nous ne testons pas les plugins.
Connecter
Commandes et configurations construites uniquement à partir de ce que déclarent ses places de marché. Nous ne les exécutons pas pour vous et ne testons pas les plugins.
En plugin Claude Code
Construit à partir de ce que déclare Community GitHub marketplaces.
/plugin marketplace add alirezarezvani/claude-skills
/plugin install autoresearch-agent@claude-code-skills
Où il est publié
| Place de marché | Statut | Version | Chiffres | Vérifié | Vu pour la première fois |
|---|---|---|---|---|---|
| Place de marché communautaire alirezarezvani/claude-skills (Ouvrir la source sur GitHub) | Référencé | 2.9.0 | Forks (GitHub): 3 939 Étoiles (GitHub): 28 149 |
Non marqué |
Chaque chiffre est celui de la place de marché et n'est jamais additionné entre places de marché. Une place de marché qui ne publie pas de chiffre n'en affiche aucun : pas de nombre ne veut pas dire zéro.
Listé dans une place de marché communautaire
| Place de marché | Dépôt | Étoiles (GitHub) |
|---|---|---|
| claude-code-skills | alirezarezvani/claude-skills | 28 149 |
Quelles places de marché nous lisons et comment nous les vérifions
Ce que contient le lot
Tel que publié sur Community GitHub marketplaces.
Skills: 6
ar-resumear-statusautoresearch-agentlooprunsetup
Agents: 1
experiment-runner
Ce qui a changé
- Un fork ou une copie est apparu sur Community GitHub marketplaces
- Est devenu un produit que nous suivons
- Community GitHub marketplaces: apparu
Forks et copies
Plugins similaires
Produits dont les noms, descriptions, outils et skills sont les plus proches de celui-ci par le sens, sur toutes les places de marché que nous lisons. La proximité n'est classée qu'à l'intérieur de cette liste.
-
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…
-
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…
-
autoresearch-discover
Scan a codebase to find where autoresearch experiments would be most valuable.
-
Autonomous LLM agent optimization using LangSmith. Spawns 5 parallel proposers in git worktrees, evaluates candidates via LangSmith experiments, and merges winners automatically…
-
Trace inspection, failure diagnosis, and replay-driven iteration for AI-powered code. Instrument your LLM calls, build regression datasets, and measure improvements — all inside…
-
evalview-mcp io.github.hidai25/evalview-mcp
Regression testing for AI agents. Golden baselines, CI/CD, LangGraph, CrewAI, OpenAI, Claude.
-
setup-doctor io.github.saxenakapil/setup-doctor
Score and improve your AI coding agent setup. Local-only, open source.
-
Test your AI applications from Claude Code using Arato Simulate
-
The context exoskeleton for AI development: optimize the entire development lifecycle to reduce token usage and context occupancy.
-
Evidence-first ML code review, paper-to-code research and reproducible GPU experiments for Claude Code
Plugins similaires par place de marché
La même question posée à chaque place de marché séparément : y a-t-il là un équivalent de ce plugin ?
Référencé sur Claude Code: claude-plugins-community (5)
-
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…
-
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 LLM agent optimization using LangSmith. Spawns 5 parallel proposers in git worktrees, evaluates candidates via LangSmith experiments, and merges winners automatically…
-
Trace inspection, failure diagnosis, and replay-driven iteration for AI-powered code. Instrument your LLM calls, build regression datasets, and measure improvements — all inside…
-
evalview-mcp io.github.hidai25/evalview-mcp
Regression testing for AI agents. Golden baselines, CI/CD, LangGraph, CrewAI, OpenAI, Claude.
Référencé sur Claude Code: claude-plugins-official (5)
-
A suite of agent skills for the full GrowthBook feature flag and experimentation lifecycle.
-
Skills for adding DeepEval evaluations, tracing, datasets, Confident AI reports, and iterative improvement loops to AI applications.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
Output.ai workflow development toolkit for Claude Code. Adds 5 specialist agents (planner, builder, debugger, prompt writer, quality reviewer), 40+ slash-command skills covering…
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
Référencé sur Claude: knowledge-work-plugins (5)
-
A suite of agent skills for the full GrowthBook feature flag and experimentation lifecycle.
-
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.
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
-
MCP server for terminal commands, file operations, and process management
Référencé sur Codex: openai-curated (openai/plugins) (5)
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
AI-powered code review in Codex, powered by CodeRabbit.
-
Inspect repositories, triage pull requests and issues, debug CI, and publish changes through a hybrid GitHub connector and CLI workflow.
Référencé sur Community GitHub marketplaces (5)
-
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…
-
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…
-
autoresearch-discover
Scan a codebase to find where autoresearch experiments would be most valuable.
-
Trace inspection, failure diagnosis, and replay-driven iteration for AI-powered code. Instrument your LLM calls, build regression datasets, and measure improvements — all inside…
-
setup-doctor io.github.saxenakapil/setup-doctor
Score and improve your AI coding agent setup. Local-only, open source.
Référencé sur 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.
-
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…
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Analyze any repository and generate AI-ready configuration — AGENTS.md, copilot-instructions.md, CI workflows, issue templates, and more. Mines your PR review patterns and creates…
-
Model, visualize, and deploy applications with Radius from the GitHub Copilot app and CLI.
Référencé sur 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.
-
Advanced Security plugin for GitHub Copilot.
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
-
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…
Référencé sur Cursor: cursor/plugins (5)
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Inspect and manage feature gates, experiments, dynamic configs, and metrics.
-
Build apps, scripts, and automations with the TypeScript SDK.
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
playwright-mcp io.github.microsoft/playwright-mcp
Playwright Tools for MCP
Référencé sur Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
testkube-mcp io.github.kubeshop/testkube-mcp
MCP server for Testkube - Manage test workflows, executions, and artifacts via AI assistants
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
MCP server for terminal commands, file operations, and process management
-
Stop stitching context for Copilot and Cursor. With Sequa MCP, your AI tools know your entire codebase and docs out of the box.
Référencé sur Official MCP Registry (5)
-
evalview-mcp io.github.hidai25/evalview-mcp
Regression testing for AI agents. Golden baselines, CI/CD, LangGraph, CrewAI, OpenAI, Claude.
-
setup-doctor io.github.saxenakapil/setup-doctor
Score and improve your AI coding agent setup. Local-only, open source.
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Tuning Engines - Governed AI Runtime
Govern model, agent, skill, and MCP workflows with policy, approvals, traces, and usage analytics.
Trouvés par le sens, et non par un code ou un dépôt commun : les plugins similaires restent des produits distincts, et ceci n'est ni un avis ni une recommandation.
C'est votre plugin ?
Ajoutez-le à vos projets et prouvez que vous le maintenez : un fichier dans son dépôt, un fichier sur votre domaine ou un enregistrement DNS. Vous recevez des alertes quand une marketplace le publie, le retire, change sa marque vérifiée ou ce qu'il demande, et son endpoint distant est surveillé comme un service web.
Données issues de Community GitHub marketplaces. Les chiffres proviennent de nos propres passages réguliers sur les places de marché, dernière lecture le . Nous ne publions aucune coordonnée des éditeurs de plugins.