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machine-learning-engineer

v1.1.0

Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.

Claude CodeAgent Plugins1 Skill

By K-Dense-AILicense: MIT199 GitHub starsUpdated 5 days ago

Directory evidence

Runtimes
Claude Code and Agent Plugins
Parsed components
1 skill or MCP entry
Source updated
Oct 2, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install machine-learning-engineer for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install machine-learning-engineer@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/K-Dense-AI/scientific-agents

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is scientific-agents/machine-learning-engineer/.

Plugin files

scientific-agents/machine-learning-engineer/
├── .claude-plugin/plugin.json
├── plugin.json
└── skills/machine-learning-engineer/SKILL.md

Included Skills1

machine-learning-engineerskills/machine-learning-engineer/SKILL.md

Think and work like an expert Machine Learning Engineer. Use when a task calls for Machine Learning Engineer judgment. Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.

Plugin manifests2

scientific-agents/machine-learning-engineer/.claude-plugin/plugin.json
{
  "name": "machine-learning-engineer",
  "version": "1.1.0",
  "description": "Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.",
  "author": {
    "name": "K-Dense-AI",
    "url": "https://github.com/K-Dense-AI"
  },
  "homepage": "https://github.com/K-Dense-AI/scientific-agents",
  "keywords": [
    "science",
    "agents-md",
    "expert-profile",
    "machine-learning-engineer"
  ]
}
scientific-agents/machine-learning-engineer/plugin.json
{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "machine-learning-engineer",
  "version": "1.1.0",
  "description": "Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.",
  "author": {
    "name": "K-Dense",
    "url": "https://www.k-dense.ai"
  },
  "homepage": "https://github.com/K-Dense-AI/scientific-agents",
  "repository": "https://github.com/K-Dense-AI/scientific-agents",
  "license": "MIT",
  "keywords": [
    "science",
    "agents-md",
    "expert-profile",
    "machine-learning-engineer"
  ]
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[machine-learning-engineer on Agent Plugins Marketplace](https://pluginsmp.com/plugins/machine-learning-engineer)