experiment-bridge

Convert experiment plans into GPU-backed deployments and collect initial results.

Updated Apr 1, 2026

Search Tags:#automation #workflow #planning #review #gpu #experiment

Ready to run

npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill experiment-bridge-wenwen555

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-bridge
Source: https://github.com/Wenwen555/ARIS-LVLM/tree/main/skills/experiment-bridge
Command: npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill experiment-bridge-wenwen555

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bridges the gap between idea discovery and execution by transforming experiment plans into running code deployments on GPU, and gathering initial results.

Core Features & Use Cases

Convert EXPERIMENT_PLAN.md into runnable experiments and deploy to GPU.
Coordinate across refine-logs documents (EXPERIMENT_PLAN.md, EXPERIMENT_TRACKER.md, FINAL_PROPOSAL.md) to ensure traceability and reproducibility.
Support deterministic execution, sanity checks, and result collection for rapid iteration and review.

Quick Start

Provide an EXPERIMENT_PLAN.md in refine-logs and invoke the skill with the plan path to start implementing experiments.

Frequently Asked Questions about experiment-bridge

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

FAQPage Schema

How do I turn an experiment plan into running GPU experiments automatically?▼

To turn an experiment plan into running GPU experiments, you provide an EXPERIMENT_PLAN.md file in refine-logs and invoke the skill to transform the plan into deployed code and gather initial results.

How does deterministic training work when deploying experiments to GPU?▼

Deterministic training ensures safe, repeatable experiments by applying configurable constants like CODE_REVIEW, AUTO_DEPLOY, and SANITY_FIRST during deployment and result capture.

Can I use markdown experiment plans to coordinate traceability across research documents?▼

Yes, the skill coordinates across refine-logs documents including EXPERIMENT_PLAN.md, EXPERIMENT_TRACKER.md, and FINAL_PROPOSAL.md to ensure experiment traceability and reproducibility throughout the workflow.

What do I need to start automating experiment deployment from markdown files?▼

You need an EXPERIMENT_PLAN.md located in the refine-logs directory, which serves as the input path to start implementing and deploying experiments to GPU infrastructure.

Does experiment-bridge support sanity checks before full GPU deployment?▼

Yes, the SANITY_FIRST configurable constant enables sanity checks before full deployment, ensuring safe and repeatable experiment execution on GPU resources.

Why is my automated experiment workflow not capturing initial results consistently?▼

Inconsistent result capture may occur if configurable constants for deterministic execution are not properly set, affecting the coordination across EXPERIMENT_TRACKER.md and FINAL_PROPOSAL.md documents.