experiment-registry

36|1|Updated Mar 24, 2026

Search Tags:#workflow #yaml #registry #benchmark #tracking #experiment #ml

Ready to run

npx skills add https://github.com/jiahao-shao1/sjh-skills --skill experiment-registry

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-registry
Source: https://github.com/jiahao-shao1/sjh-skills/tree/main/skills/experiment-registry
Command: npx skills add https://github.com/jiahao-shao1/sjh-skills --skill experiment-registry

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficiently manage the lifecycle of ML experiments by storing metadata in a human-readable YAML registry, making it easy to track configurations, results, and statuses across runs.

Core Features & Use Cases

Registry-driven experiment metadata with simple register/update flows.
Benchmark and result tracking across datasets, eval modes, and steps.
Workflow integration with the exp CLI for project initialization, listing, and reporting.

Quick Start

Add a YAML entry describing your experiment to the registry and run the exp register command to establish it.

Frequently Asked Questions about experiment-registry

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I track ML experiments using a YAML registry?▼

To track ML experiments with a YAML registry, you add a YAML entry describing your experiment and run the `exp register` command. This stores metadata in a human-readable format, making it easy to manage configurations and statuses across runs.

What is a YAML-based experiment registry for machine learning?▼

A YAML-based experiment registry is a human-readable system for managing the lifecycle of ML experiments. It stores configurations, results, and statuses in YAML files, enabling simple register and update flows to track benchmarks across different runs.

Do I need the exp CLI to manage ML experiment configurations?▼

Yes, you need the `exp` CLI installed to use this YAML registry workflow. The project root must contain an `exp.config.yaml` file along with per-experiment YAML entries to initialize, list, and report on your machine learning runs.

What's the best way to manage ML benchmarks across different datasets?▼

The best way to manage ML benchmarks across datasets is using a registry-driven approach that tracks results and evaluation modes in YAML files. This allows you to update and compare benchmark performance across multiple experiment runs efficiently.

Why use YAML files for machine learning experiment tracking instead of a database?▼

Using YAML files for experiment tracking provides a human-readable, lightweight format for storing metadata without database overhead. It simplifies version control integration and makes reviewing configurations and benchmark results across runs straightforward.

How do I initialize a project for ML experiment tracking with YAML?▼

To initialize a project for ML experiment tracking, you set up an `exp.config.yaml` file in your project root and create per-experiment YAML entries. You then use the `exp` CLI to establish the registry and register your configurations.