skills.rest
antondelafuente
antondelafuenteCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

design-experiment

Design GPU experiments with purpose, metrics, comparability, and data collection specifications.

Updated Jun 11, 2026
Search Tags:#collaboration#validation#machine learning#experiment design#GPU execution
One-click install
npx skills add https://github.com/antondelafuente/automated-researcher --skill design-experiment-antondelafuente
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: design-experiment
Source: https://github.com/antondelafuente/automated-researcher/tree/main/plugins/experiment-lifecycle/skills/design-experiment
Command: npx skills add https://github.com/antondelafuente/automated-researcher --skill design-experiment-antondelafuente

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires verify-claims, run-experiment, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you design GPU experiments with a researcher and then dispatch them for execution, reducing the complexity and time required for experimental workflows.

Core Features & Use Cases

  • Collaborative Design: Propose experiments, iterate with a researcher, and create a comprehensive design specification.
  • Pre-launch Gates: Validate the experiment's data trustworthiness and claims before execution.
  • Executor Dispatch: Send the design to a fresh-context executor for autonomous execution.
  • Use Case: Design an experiment to compare different machine learning models on a new dataset, using this Skill to ensure the experiment's design is sound and then dispatching it for execution.

Quick Start

Use the design-experiment skill to design an experiment for the dataset 'model-training-data.csv'.

Frequently Asked Questions about design-experiment

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

FAQPage Schema
How do I design a GPU experiment for machine learning model validation?▼

To design a GPU experiment, define the purpose, metrics, comparability, and data collection specifications collaboratively, then validate trustworthiness and claims before dispatching for execution.

What is the best way to validate machine learning experiment claims before GPU execution?▼

Validating machine learning experiment claims requires pre-launch gates that check data trustworthiness and design specifications before dispatching to an executor for GPU execution.

Can I collaborate with a researcher to define metrics and data collection for experiment design?▼

Yes, you can collaborate with a researcher to propose experiments, iterate on the design, and create a comprehensive specification defining metrics and data collection for GPU execution.

How do I dispatch an experiment design to an executor for autonomous GPU execution?▼

Dispatch an experiment design by sending the validated specification to a fresh-context executor, which autonomously handles the GPU execution after pre-launch validation gates pass.

Do I need to verify claims before running a machine learning experiment on a GPU?▼

Yes, you need to verify claims before running a machine learning experiment, as pre-launch gates validate data trustworthiness and design specifications prior to dispatching for GPU execution.

Related Skills

Explore More Software Engineering Skills→
kaiohenricunhakaiohenricunha

veracity-audit

Audit data pipelines for source reliability, scoring math, and quality gates.

Community
Advanced
LambenthanLambenthan
77

/exp-run

Deploy and collect machine learning experiments from wiki plans across local and SSH environments.

Community
Advanced
dogekikidogekiki

experiment-plan

Transform research proposals into claim-driven experiment roadmaps with ablation matrices and baseline comparisons.

Community
Advanced
satsuki-64satsuki-64
2

experiment-bridge

Convert EXPERIMENT_PLAN.md files into runnable GPU-backed experiments with automated deployment.

Community
Advanced