experiment-bridge

Parse EXPERIMENT_PLAN.md to implement, deploy, and collect GPU metrics.

1|Updated Mar 26, 2026

Search Tags:#automation #workflow #planning #reproducibility #gpu-deployment #experiment-bridge

Ready to run

npx skills add https://github.com/Lingrongye/federated-learning --skill experiment-bridge-lingrongye

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-bridge
Source: https://github.com/Lingrongye/federated-learning/tree/main/Auto-claude-code-research-in-sleep/skills/experiment-bridge
Command: npx skills add https://github.com/Lingrongye/federated-learning --skill experiment-bridge-lingrongye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bridges the gap between idea discovery and execution by turning brainstormed plans into runnable experiments with initial results, reducing manual handoffs and debugging time.

Core Features & Use Cases

Automates end-to-end experiment workflow from planning to deployment on GPU.
Deploys and monitors experiments, collects logs and initial metrics for quick review.
Enables reproducible results by fixing seeds and exporting outputs for later analysis.

Quick Start

Provide an EXPERIMENT_PLAN.md and invoke the skill to implement, deploy, and collect initial results.

Frequently Asked Questions about experiment-bridge

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

FAQPage Schema

How do I automate running experiments from a markdown plan on GPU?▼

To automate running experiments from a markdown plan on GPU, you provide an EXPERIMENT_PLAN.md file. The skill parses this file to automatically implement, deploy, and run the planned experiments while collecting initial metrics and logs.

How can I ensure reproducible results when deploying automated experiments?▼

To ensure reproducible results when deploying automated experiments, the workflow fixes random seeds during execution. It also exports all collected metrics and outputs in structured JSON and CSV formats for later consistent review.

What is the best way to convert an experiment plan into initial metrics automatically?▼

The best way to convert an experiment plan into initial metrics automatically is by parsing EXPERIMENT_PLAN.md. The process bridges idea discovery to execution by deploying the plan on GPU and collecting initial results with exported logs.

Do I need to write code to deploy and monitor GPU experiments from a planning document?▼

No, you do not need to write deployment code manually to run GPU experiments from a planning document. You simply provide the plan file, and the automation workflow handles the implementation, deployment, monitoring, and metric collection.

What format should experiment outputs be in for later review and analysis?▼

Experiment outputs for later review and analysis should be in JSON and CSV formats. The automated workflow saves collected initial metrics and logs in these structured formats to enable reproducible results and quick evaluation.

Can I use this workflow to reduce manual debugging time when testing new ideas?▼

Yes, you can use this workflow to reduce manual debugging time when testing new ideas. It bridges the gap between discovery and execution by turning brainstormed plans into runnable experiments, minimizing manual handoffs and debugging.