experiment-dse

Automate hyperparameter design-space exploration and generate DSE reports.

7|1|Updated Mar 19, 2026

Search Tags:#automation #hyperparameters #config-management #ml-research #experiment-plan #design-space-exploration

Ready to run

npx skills add https://github.com/Shiien/Self-Evolved-Research-Framework --skill experiment-dse

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-dse
Source: https://github.com/Shiien/Self-Evolved-Research-Framework/tree/main/skills/experiment-dse
Command: npx skills add https://github.com/Shiien/Self-Evolved-Research-Framework --skill experiment-dse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design-space exploration for hyperparameters across ML experiments helps researchers discover optimal configurations efficiently.

Core Features & Use Cases

Generate a YAML-dicated config list from an experiment plan or parameter ranges.
Coordinate batched execution via experiment-run and produce a comprehensive DSE report with best config and sensitivity analysis.
Trigger downstream tasks and provide a structured path from experiment-plan to experiment-run.

Quick Start

Start a DSE by supplying an experiment-plan and config.yaml budget, then let the system generate configs.yaml, run batches via experiment-run, and produce a final report.

Frequently Asked Questions about experiment-dse

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

FAQPage Schema

How do I automate hyperparameter tuning across multiple ML experiments?▼

Automate hyperparameter tuning by supplying an experiment-plan or parameter ranges alongside a config budget. The system generates a configs.yaml list, coordinates batched execution, and compiles a comprehensive DSE report with the best configuration and sensitivity analysis.

What is design-space exploration in machine learning research?▼

Design-space exploration in ML research evaluates multiple hyperparameter configurations to discover optimal setups efficiently. It generates a YAML-dictated config list from parameter ranges, runs batched experiments within budget constraints, and produces a final sensitivity analysis report.

How do I run batched ML experiments with budget constraints?▼

Run batched ML experiments within budget constraints by providing an experiment-plan and a config.yaml budget. The workflow generates parameter configurations, triggers downstream execution tasks via experiment-run, and compiles the final results into a structured report.

Can I generate a config list from parameter ranges for ML experiments?▼

Yes, generate a config list from parameter ranges for ML experiments. Supplying parameter ranges and a config budget allows the system to automatically dictate and output a structured configs.yaml file for downstream batched execution and sensitivity analysis.

What is the best way to compile a sensitivity analysis report for hyperparameters?▼

Compile a sensitivity analysis report by orchestrating end-to-end design-space exploration. After generating configurations and running batched experiments via experiment-run, the system automatically produces a comprehensive DSE report highlighting the best config and parameter sensitivities.