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experiment-bridge

Parse EXPERIMENT_PLAN.md and deploy experiments across GPUs with JSON/CSV logging.

Updated Apr 8, 2026
Search Tags:#automation#workflow#planning#deployment#GPU#experiment
One-click install
npx skills add https://github.com/KYRIE66nb/codex-omx-public-config --skill experiment-bridge-kyrie66nb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-bridge
Source: https://github.com/KYRIE66nb/codex-omx-public-config/tree/main/home/.codex/skills/experiment-bridge
Command: npx skills add https://github.com/KYRIE66nb/codex-omx-public-config --skill experiment-bridge-kyrie66nb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bridge experiments from plan to deployed runs, automating execution and result collection to accelerate iteration.

Core Features & Use Cases

  • Parse EXPERIMENT_PLAN.md and extract milestones, datasets, metrics, and setup details.
  • Implement and deploy experiments across GPUs, with sanity checks and parallel run management.
  • Collect, log, and summarize initial results to feed auto-review loops and decision making.

Quick Start

Initialize the bridge with your EXPERIMENT_PLAN.md to automatically translate plans into runnable experiments.

Frequently Asked Questions about experiment-bridge

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

FAQPage Schema
How do I automate experiment deployment from a planning document?▼

To automate experiment deployment, you can parse an EXPERIMENT_PLAN.md file to extract milestones and metrics, then generate implementation scripts to run experiments across GPUs automatically.

How do I collect and log experiment results to JSON or CSV for analysis?▼

You can collect and log experiment results to JSON or CSV by running automated deployments with sanity checks, which summarize initial results into structured formats for downstream analysis.

Can I manage parallel GPU deployments for multiple experiments?▼

Yes, you can manage parallel deployments across GPUs by translating an EXPERIMENT_PLAN.md into runnable code, allowing simultaneous execution and result collection for multiple experiments.

What is the best way to bridge experiment planning to running code?▼

Bridging experiment planning to running code involves parsing an EXPERIMENT_PLAN.md to extract setup details, generating implementation scripts, and executing automated deployments with sanity checks.

Do I need an EXPERIMENT_PLAN.md to automate experiment workflows?▼

Yes, an EXPERIMENT_PLAN.md is required to automate experiment workflows, as the bridge parses this file to extract milestones, datasets, metrics, and setup details for deployment.

Why run sanity checks during automated experiment deployment?▼

Running sanity checks during automated experiment deployment ensures generated implementation scripts execute correctly across GPUs before logging initial results to JSON or CSV for analysis.

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