Implement planned experiments from EXPERIMENT_PLAN.md into GPU training runs.
npx skills add https://github.com/Mang30/myskills --skill experiment-bridge-mang30
Please help me install this Agent Skill. Skill: experiment-bridge Source: https://github.com/Mang30/myskills/tree/main/skills/experiment-bridge Command: npx skills add https://github.com/Mang30/myskills --skill experiment-bridge-mang30
It turns an experiment plan into runnable training/evaluation code, deploys experiments to GPU, and collects initial results so you can move from design to evidence quickly.
Use the experiment-bridge skill to run the planned experiments by executing: /experiment-bridge "refine-logs/EXPERIMENT_PLAN.md".
High-intent search queries and answers about installing and using this skill.
To automate GPU deployment for planned experiments, you can parse an EXPERIMENT_PLAN.md file to generate runnable training and evaluation code, route jobs through an experiment queue, and deploy milestones in the correct run order to collect initial results.
Turning an experiment plan into runnable code involves reading proposal context and milestone requirements to automatically generate the necessary training, evaluation, and logging scripts for immediate GPU deployment.
To collect structured JSON and CSV results from training runs, you can parse log outputs, check success criteria against ground-truth evaluation metrics, and automatically update the experiment tracker for the next review loop.
Yes, you can perform cross-model code review before GPU deployment by enabling pre-deploy validation guardrails, which also include running a sanity stage first to ensure ground-truth evaluation is correctly configured.
You do not need a specific experiment tracker to automate evaluation metrics collection; the process parses JSON/CSV/log outputs, checks success criteria, and prepares a structured handoff to update your existing tracking workflow.
Limitations of automating milestone-based research workflows include the requirement for a pre-defined EXPERIMENT_PLAN.md and proposal context, as the automation cannot generate initial experiment designs or operate without structured milestone definitions.
lightrain-aImplement experiment plans into deployable code and collect initial results.
Shallow-WConvert experiment plans into executable training code and deploy GPU runs.
goupup-aiConvert ML experiment plans into runnable code, GPU jobs, and initial results.
Mang30Generate a claim-driven experiment roadmap with run order and compute estimates.
