autoresearch
Set up autonomous goal-driven optimization — creates a GOAL.md fitness function and runs an improvement loop
Lot de plugins · par James S Tayler
Aperçu
autoresearch est un lot de plugins pour les outils de code IA publié par James S Tayler. Places de marché que nous lisons et qui le référencent : 1 (Community GitHub marketplaces). Nous l'avons vu pour la première fois le 2026-10-22 et n'avons enregistré aucun changement depuis. Selon notre passage sur les places de marché du 2026-10-23.
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 james-s-tayler/lazy-developer
/plugin install autoresearch@james-s-tayler-skillsOù il est publié
| Place de marché | Statut | Version | Chiffres | Vérifié | Vu pour la première fois |
|---|---|---|---|---|---|
| Place de marché communautaire james-s-tayler/lazy-developer (Ouvrir la source sur GitHub) | Référencé | 1.1.0 | Forks (GitHub): 0 Étoiles (GitHub): 12 |
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) |
|---|---|---|
| james-s-tayler-skills | james-s-tayler/lazy-developer | 12 |
Quelles places de marché nous lisons et comment nous les vérifions
Ce que contient le lot
Tel que publié sur Community GitHub marketplaces.
Skills: 3
autoresearchjob-securitylazy-developer
Ce qui a changé
- Est devenu un produit que nous suivons
- Community GitHub marketplaces: apparu
Peut-être le même plugin
Des fiches qui partagent un indice avec celle-ci, mais pas assez pour les traiter comme le même produit.
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 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 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…
-
Self-correcting loop engineering and automated codebase research.
-
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…
-
Autonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/ar:setup, /ar:ar-run, /ar:loop, /ar:ar-status, /ar:ar-resume), 8 built-in evaluators…
-
Structured experiment-driven code optimization using tree search and parallel subagents
-
The PIV loop (plan, implement, validate, review, commit, PR) plus priming, planning, worktrees, and the meta-skills for building your own AI Layer.
-
Skill auto-evolution engine - combines skill-creator evaluation + autoresearch iteration principles to automatically create, evaluate, and evolve Claude Code skills through gated…
-
20 enterprise-grade skills for autonomous AI/ML research lab operations. Includes 10 custom gap-analysis skills (research-commander, agent-debugger, model-evaluator…
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 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 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…
-
20 enterprise-grade skills for autonomous AI/ML research lab operations. Includes 10 custom gap-analysis skills (research-commander, agent-debugger, model-evaluator…
-
A self-improving agent skill pipeline that prevents common AI coding agent failures. Includes 6 skills (plan-interview, intent-framed-agent, context-surfing, simplify-and-harden…
-
Advanced debugging, code review, deep research, technical documentation, creative ideation, and semi-autonomous development workflows with parallel theory validation, team-based…
Référencé sur Claude Code: claude-plugins-official (5)
-
Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, 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…
-
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
-
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…
-
Find the right NVIDIA skill for GPU acceleration, CUDA, AI agents, data loading, training, inference, robotics, Physical AI, Omniverse, simulation, and other NVIDIA-powered…
Référencé sur Claude: knowledge-work-plugins (5)
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Streamline engineering workflows — standups, code review, architecture decisions, incident response, and technical documentation. Works with your existing tools or standalone.
-
Bring Qodo's codebase intelligence, coding standards, and code review into your coding agent.
-
Official Buildkite skills for Claude Code, Cursor, and other AI coding agents — pipelines, migration, preflight, agent runtime, CLI, and API
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.
-
An agentic skills framework & software development methodology that works: planning, TDD, debugging, and collaboration workflows.
-
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.
Référencé sur Community GitHub marketplaces (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 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…
-
Self-correcting loop engineering and automated codebase research.
-
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…
Référencé sur 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…
-
Meta prompts that help you discover and generate curated GitHub Copilot agents, instructions, prompts, and skills.
-
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…
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
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.
-
workiq-productivity
WorkIQ productivity skills powered by the local WorkIQ CLI — read-only insights across email, meetings, calendar, Teams, SharePoint, projects, and people.
-
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)
-
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.
-
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…
-
Build apps, scripts, and automations with the TypeScript SDK.
Référencé sur 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.
-
Stop stitching context for Copilot and Cursor. With Sequa MCP, your AI tools know your entire codebase and docs out of the box.
-
Sequential Thinking (Reference)
Dynamic and reflective problem-solving through thought sequences.
-
Tools and resources for writing Effect code in Typescript.
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
Référencé sur Official MCP Registry (5)
-
Grow your skills with AI: per-skill coaching inside Claude, ChatGPT, Cursor and Codex
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Autonomous factory that finds real developer pain points and builds small paid AI tools to fix them.
-
Checks whether your idea already exists before your agent builds it. Searches GitHub, npm, Python.
-
llmref org.llmref/llmref
Versioned documentation registry and semantic search for AI tools and coding assistants.
Référencé sur PyPI (MCP servers) (5)
-
Let one coding-agent CLI consult another: Codex, Claude Code, OpenCode, Copilot or Antigravity.
-
lineai-mcp-server io.github.lineai-intelligence/lineai-mcp-server
An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
-
repo-graph io.github.James-Chahwan/repo-graph
Structural graph map of any codebase so LLMs navigate by structure, not guesswork.
-
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.
Référencé sur 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.
-
speclock io.github.sgroy10/speclock
AI Constraint Engine — enforces CLAUDE.md and .cursorrules as laws. 51 MCP tools.
-
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.
-
One workspace for every AI coding assistant. Shared tasks, memory, and governance.
-
sub-agents-mcp io.github.shinpr/sub-agents-mcp
MCP server for delegating tasks to specialized AI sub-agents across coding tools
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.
Vous voulez cette marque pour votre plugin dans notre catalogue ?
Prouvez que vous le maintenez avec un fichier dans son dépôt, un fichier sur votre domaine ou un enregistrement DNS, et vous pourrez afficher cette marque sur la fiche, avec des bannières à jour pour votre site et votre README. Comment se passe la vérification
Vous recevez aussi 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.