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experiment-bridge

Convert EXPERIMENT_PLAN.md into runnable experiment code and GPU deployment.

Updated Mar 1, 2026
Search Tags:#automation#workflow#planning#research#gpu#reproducibility#experiment
One-click install
npx skills add https://github.com/hve4638/hve-cc-marketplace --skill experiment-bridge-hve4638
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-bridge
Source: https://github.com/hve4638/hve-cc-marketplace/tree/main/aris/skills/skills-codex/experiment-bridge
Command: npx skills add https://github.com/hve4638/hve-cc-marketplace --skill experiment-bridge-hve4638

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bridges idea discovery and automated evaluation by turning an EXPERIMENT_PLAN.md into executable experiments, deploying them to GPU resources, and collecting initial results.

Core Features & Use Cases

  • Parse EXPERIMENT_PLAN.md and related inputs (EXPERIMENT_TRACKER.md, FINAL_PROPOSAL.md) to generate runnable experiment scripts.
  • Implement experiment code, configure hyperparameters, and ensure reproducibility with fixed seeds.
  • Deploy experiments to GPUs automatically, with a sanity check and parallel execution where possible.
  • Collect, validate, and export initial results for auto-review and decision making.

Quick Start

Provide an EXPERIMENT_PLAN.md and run the bridge 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 turn an EXPERIMENT_PLAN.md into runnable code for GPU deployment?▼

You turn an EXPERIMENT_PLAN.md into runnable code by using an automation bridge that parses the markdown, generates executable experiment scripts, configures hyperparameters, and deploys them directly to GPU resources.

Can I ensure research reproducibility when automating experiment workflows?▼

You can ensure research reproducibility during experiment automation by configuring fixed seeds and generating runnable experiment scripts that execute parallel GPU deployments with consistent hyperparameter settings.

What is the best way to deploy multiple experiments to GPUs automatically?▼

The best way to deploy multiple experiments to GPUs automatically is to parse an EXPERIMENT_PLAN.md into runnable code, run a sanity check, and execute the configured experiments in parallel where possible.

Do I need a base repository to automate experiment execution from a markdown plan?▼

You do not need a mandatory base repository to automate experiment execution, but you can provide optional overrides to a base repository alongside your required EXPERIMENT_PLAN.md input for customized deployment.

How are initial experiment results collected and validated for auto-review?▼

Initial experiment results are collected and validated after automated GPU execution, then exported and saved to refine-logs to enable auto-review and decision making for tracking multiple experiments.

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