experiment-tracker
Generate ML experiment tracking with MLflow
Plugin bundle · by barnburner121
About
experiment-tracker is a plugin bundle for AI coding tools published by barnburner121. Marketplaces we read that list it: 1 (Community GitHub marketplaces). We first saw it on 2026-10-14 and have recorded no change since. As of our marketplace pass on 2026-10-15.
What it asks for
- Runs on your machine
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 barnburner121/claude-plugin-marketplace
/plugin install experiment-tracker@barnburner121-plugins
Where it is published
| Marketplace | Status | Version | Figures | Verified | First seen |
|---|---|---|---|---|---|
| Community marketplace barnburner121/claude-plugin-marketplace (Open the source on GitHub) | Listed | 1.0.0 | Forks (GitHub): 0 Stars (GitHub): 3 |
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) |
|---|---|---|
| barnburner121-plugins | barnburner121/claude-plugin-marketplace | 3 |
What the bundle contains
As published on Community GitHub marketplaces.
MCP servers: 1
plugin-hub
Skills: 1
experiments
What changed
- Became a product we track
- Community GitHub marketplaces: appeared
Possibly the same plugin
Listings that share a signal with this one but not enough for us to treat them as the same product.
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.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
mlflow io.github.us-all/mlflow
MLflow MCP — experiments, runs, registered models, versions, traces, assessments (MLflow 3)
-
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…
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
Generate ML model monitoring and drift detection
-
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 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…
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
weightsbiases-mcp io.github.mrfentmen/weightsbiases-mcp
MCP server for Weights & Biases: profile, projects, runs. Needs a free API key.
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…
-
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…
-
Skills to support Machine Learning experimentation using the Python ecosystem.
-
Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
Has a listing on Claude Code: claude-plugins-official (5)
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
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…
-
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.
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…
-
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.
-
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 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…
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
Generate ML model monitoring and drift detection
-
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 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…
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.
-
Create, edit, run, save, and checkpoint Jupyter notebooks in an interactive Copilot canvas.
-
Model, visualize, and deploy applications with Radius from the GitHub Copilot app and CLI.
-
Arize AX platform skills for LLM observability, evaluation, and optimization. Includes trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations…
-
Use Datadog directly in Copilot / VS Code through a preconfigured Datadog MCP server. Query logs, metrics, traces, dashboards, and more through natural conversation.
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.
-
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…
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
-
Complete bundle: all Microsoft Skills for Fabric for developers and consumers.
Has a listing on Cursor: cursor/plugins (5)
-
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.
-
Build apps, scripts, and automations with the TypeScript SDK.
-
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…
-
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.
Has a listing on 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.
-
Tools for interacting with Hugging Face models, datasets, research papers, and more.
-
Analyze documents, images, videos, and audio.
Has a listing on Official MCP Registry (5)
-
mlflow io.github.us-all/mlflow
MLflow MCP — experiments, runs, registered models, versions, traces, assessments (MLflow 3)
-
Automated AI research toolkit: hypothesis generation, experiments, and paper writing
-
weightsbiases-mcp io.github.mrfentmen/weightsbiases-mcp
MCP server for Weights & Biases: profile, projects, runs. Needs a free API key.
-
pramana-mcp io.github.loopg/pramana-mcp
Read your recorded AI agent runs, and what changed when you swapped a model or prompt.
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
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.