Orchestra-Research/AI-Research-SKILLs
mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Before you install
- Last update
- Files in folder
- 4
- Scripts
- No script files found
- SKILL.md size
- 15 KB (about 3,900 tokens of context each time it loads)
- License
- MIT
- Source on GitHub
- Orchestra-Research/AI-Research-SKILLs
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
Install
npx skills add Orchestra-Research/AI-Research-SKILLs@mlflowRuns in a terminal with Node.js 18 or newer. Add -y to skip the prompts and -g to install for every project instead of this one.
Install for your agent
npx skills add Orchestra-Research/AI-Research-SKILLs@mlflow --agent claude-code -y- Project install writes to
.claude/skills/mlflow- Global install writes to
~/.claude/skills/mlflow
Start a new session afterwards so the agent picks the skill up. In Claude Code you can also call it directly with /mlflow.
What it does
MLflow: ML Lifecycle Management Platform When to Use This Skill Use MLflow when you need to: - Track ML experiments with parameters, metrics, and artifacts - Manage model registry with versioning and stage transitions - Deploy models to various platforms (local, cloud, serving) - Reproduce experiments with project configurations - Compare model versions and performance metrics - Collaborate on ML projects with team workflows - Integrate with any ML framework (framework-agnostic) Users: 20,000+ organizations GitHub Stars: 23k+ License: Apache 2.0 Installation Quick Start Basic Tracking…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
Categories
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