create-experiment
Scaffold a Dokimos Experiment with dataset, task, evaluators, and reporter
Plugin bundle · by Dokimos Contributors
About
create-experiment is a plugin bundle for AI coding tools published by Dokimos Contributors. Marketplaces we read that list it: 1 (Community GitHub marketplaces). The last change we recorded was on 2026-10-19. As of our marketplace pass on 2026-10-24.
What it asks for
- Installs as a plugin bundle
None of the marketplaces we read publishes its tools or the settings it asks for.
What it declares, asks for and runs, in full
Only install plugins from developers you trust. We list what marketplaces publish and do not test plugins.
Works with
- Claude Code
From the marketplaces that list it and the MCP transport it declares, as each client documents them. We do not test plugins.
Connect
Commands and configs built only from what its marketplaces declare. We do not run them for you and do not test plugins.
As a Claude Code plugin
Built from what Community GitHub marketplaces declares.
/plugin marketplace add dokimos-dev/dokimos
/plugin install create-experiment@dokimos
Where it is published
| Marketplace | Status | Version | Figures | Verified | First seen |
|---|---|---|---|---|---|
| Community marketplace dokimos-dev/dokimos (Open the source on GitHub) | Listed | 0.1.0 | Forks (GitHub): 5 Stars (GitHub): 60 |
Not marked |
Each figure is the marketplace's own and is never added up across marketplaces. A marketplace that publishes no figure shows none: no number is not zero.
Listed in one community marketplace
| Marketplace | Repository | Stars (GitHub) |
|---|---|---|
| dokimos | dokimos-dev/dokimos | 60 |
How its figures have moved
- 2026-10-195
- 2026-10-245
Each dot is a day we read a different number; flat stretches are days the number did not move. Figures of different marketplaces are never added up.
What the bundle contains
As published on Community GitHub marketplaces.
Skills: 1
create-experiment
What changed
- Became a product we track
- Community GitHub marketplaces: appeared
Similar plugins
Products whose own names, descriptions, tools and skills are closest in meaning to this one, on every marketplace we read. Closeness is ranked within this list only.
-
Autonomous experiment loop — modify, test, keep/discard, repeat forever
-
Create evaluation datasets in JSON, CSV, or JSONL format for Dokimos
-
Feature metadata management for multimodal data and ML pipelines. Provides guidance on feature definitions, versioning, metadata stores, and testing.
-
Structured protocols for feature exploration and development workflows
-
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…
-
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…
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
-
Plan small custom models and fine-tunes: check the Hub, research options, write the spec in a planning repo.
-
Scaffolding, running, documenting, and publishing AI evaluations. Ships skills and commands for setting up eval workspaces, creating custom evals (or adapting existing…
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Similar plugins by marketplace
The same question asked of one marketplace at a time: is there an analog of this plugin there?
Has a listing on 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…
-
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
-
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…
-
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…
Has a listing on 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.
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
A suite of agent skills for the full GrowthBook feature flag and experimentation lifecycle.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
Has a listing on 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…
-
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.
-
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
Has a listing on 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.
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
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…
Has a listing on Community GitHub marketplaces (5)
-
Autonomous experiment loop — modify, test, keep/discard, repeat forever
-
Create evaluation datasets in JSON, CSV, or JSONL format for Dokimos
-
Feature metadata management for multimodal data and ML pipelines. Provides guidance on feature definitions, versioning, metadata stores, and testing.
-
Structured protocols for feature exploration and development workflows
-
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…
Has a listing on 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.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Model, visualize, and deploy applications with Radius from the GitHub Copilot app and CLI.
-
Skills and interactive Copilot canvas for designing, configuring, testing and deploying agents to Microsoft Foundry.
-
Design, build, review, and publish GitHub Skills-style exercises with one coordinating agent and five Agent Skills for learner journeys, workflow-backed validation, and release…
Has a listing on Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
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…
-
mobile-app
Build and deploy Power Apps code apps for mobile using Expo (React Native), with native device functionality and Power Platform connectors
-
code-apps-preview
Build and deploy Power Apps code apps using React, Vite, and Power Platform connectors.
Has a listing on 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.
-
Inspect and manage feature gates, experiments, dynamic configs, and metrics.
-
Ask Databricks Genie questions about your workspace's data in plain language.
-
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…
Has a listing on Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Analyze documents, images, videos, and audio.
-
testkube-mcp io.github.kubeshop/testkube-mcp
MCP server for Testkube - Manage test workflows, executions, and artifacts via AI assistants
-
Tools for interacting with Hugging Face models, datasets, research papers, and more.
-
Keboola MCP Server is an open-source bridge between your Keboola project and modern AI tools.
Has a listing on Official MCP Registry (5)
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
-
Run data annotation and evaluation tasks with vetted domain experts: create, publish, get results.
-
Research graph MCP for hypotheses, goals, runs, source quality, audits, and generated maps.
Has a listing on PyPI (MCP servers) (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Read, measure and explain LLM eval runs of a digline suite. Cannot promote a baseline.
-
replicate com.mcparmory/replicate
Run AI models, create deployments, and manage predictions via cloud API
-
fiftyone-mcp-server io.github.AdonaiVera/fiftyone-mcp-server
Control FiftyOne computer vision datasets through AI assistants using 80+ operators.
-
Langfuse MCP server for accessing and analyzing telemetry data via natural language
Has a listing on npm registry (MCP servers) (5)
-
One workspace for every AI coding assistant. Shared tasks, memory, and governance.
-
genexus io.github.lennix1337/genexus
Read, edit, and analyze GeneXus 18 KB objects from Claude, Cursor, and AI agents.
-
Compile documents into a cited knowledge wiki. Retrieve evidence, query, and review via local MCP.
-
Live AWS, database & Kafka infrastructure context for AI coding assistants via MCP.
-
MCP server for n8n workflow management -- view, create, sync and manage workflows via AI.
Found by meaning, not by shared code or a shared repository: similar plugins stay separate products, and this is not a review or a recommendation.
Want this mark on your plugin in our catalog?
Prove you maintain it with a file in its repository, a file on your domain or a DNS record, and you can show this mark on the card, with live banners for your site and README. How verification works
You also get alerts when a marketplace lists it, drops it, changes its verified mark or what it asks for, and its remote endpoint goes on monitoring as a web service.
Data comes from public marketplace files in GitHub repositories of their authors. Figures come from our own regular passes over the marketplaces, last read on . We publish no contact details for plugin publishers.