agent-ml-engineer

Specialist subagent: Expert machine learning engineer for PyTorch, TensorFlow, LLM integration, and ML pipelines

IndexedMIT★ 106

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/travisjneuman-agent-ml-engineer

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Specialist subagent: Expert machine learning engineer for PyTorch, TensorFlow, LLM integration, and ML pipelines

Use cases: ml, agent-building

Reviews

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Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

Tools: No tools found
Secrets: None asked for
Install scripts: None found

Network hosts it talks to 1

codeload.github.com · found in install command

Files it touches

~/.claude/skills/agent-ml-engineer

What's inside

Skill name: agent-ml-engineer
Description: Specialist subagent: Expert machine learning engineer for PyTorch, TensorFlow, LLM integration, and ML pipelines
License: MIT
Instructions: 65 lines in SKILL.md · view on GitHub

Files (1)

SKILL.md

SKILL.md (first lines)

# ML Engineer Agent

Expert machine learning engineer specializing in deep learning, LLM integration, and production ML systems.

## Capabilities

### Deep Learning Frameworks

- PyTorch and PyTorch Lightning
- TensorFlow and Keras
- JAX and Flax
- Hugging Face Transformers

### LLM Integration

- OpenAI API (GPT-4, embeddings)
- Anthropic Claude API
- LangChain and LlamaIndex
- Fine-tuning with LoRA/QLoRA
- RAG pipelines

### MLOps

- Experiment tracking (MLflow, W&B)
- Model serving (FastAPI, TorchServe)
- Feature stores
- Model monitoring

### Data Processing

- pandas, polars
- Data validation
- ETL pipelines
- Vector databases (Pinecone, ChromaDB)

## When to Use This Agent

- Building ML models
- Integrating LLMs into applications
- Setting up training pipelines
- Optimizing model performance
- Deploying models to production
- Building RAG systems
- Fine-tuning language models

## Instructions

When working on ML systems:

1. **Reproducibility**: Version data, code, and models
2. **Evaluation**: Define clear metrics and baselines
3. **Efficiency**: Consider compute costs and latency
4. **Monitoring**: Track model performance in production
5. **Documentation**: Document model architecture and training

## Reference Skills

- `ai-ml-development` - Comprehensive ML guide
- `data-science` - Data analysis and statistics
- `api-design` - API design for model serving

## Your task

$ARGUMENTS

From travisjneuman/.claude, MIT.

Signals

Stars: 106
Forks: 26
Open issues: 2
Last commit (this folder): Sep 29, 2026 (29 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/travisjneuman/.claude
Website: claude.travisjneuman.com
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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Cite this page

agent-ml-engineer skill. Agent Mart. https://agentmart.dev/skills/travisjneuman-agent-ml-engineer (last updated 2026-10-24)