Run autonomous ML experiment loops on a remote VPS via SSH.
npx skills add https://github.com/mammut001/pipi-shrimp-agent --skill autoresearch-mammut001
Please help me install this Agent Skill. Skill: autoresearch Source: https://github.com/mammut001/pipi-shrimp-agent/tree/main/src-tauri/skills/autoresearch Command: npx skills add https://github.com/mammut001/pipi-shrimp-agent --skill autoresearch-mammut001
Autonomous ML experimentation is error-prone and time-consuming when done manually: this skill coordinates a remote VPS, session files, and versioned code changes to run iterative experiments with logs and metrics.
Start a new AutoResearch session by placing a session.md at the default path and then let the agent run iterative experiments on the remote VPS.
High-intent search queries and answers about installing and using this skill.
Autonomous ML experiments on a remote VPS require an orchestration loop that executes iterative training, evaluates results, and patches code via SSH. This skill coordinates these steps using a user-supplied session file to drive autonomous runs and record metrics.
An autonomous ML experiment loop iteratively proposes code changes, executes training on a remote VPS, and parses results to evaluate hypotheses. It automatically commits successful changes or rolls back failed ones while maintaining a reproducible experiment history.
To start an autonomous experiment session, place a session.md file at the default path to define constraints and guidelines. The agent reads this session file to orchestrate training, fetch and edit code, and run evaluations on the VPS automatically.
Yes, SSH access is required to run iterative model training on a VPS using this skill. It relies on SSH-driven orchestration to fetch and patch remote training scripts, execute the training process, and handle automatic code commits or rollbacks.
Automated ML training handles failed experiments by parsing training results and automatically rolling back code changes. It enforces session-defined constraints and guardrails to maintain a reproducible experiment history and preserve stable training code.
Avi977Automate GitHub-to-VPS deployment with SSH keys, workflows, and health checks.
gyy0592Generate detailed 13-section markdown experiment reports from flame_moonshot training logs and metrics.
afel6Track ML experiments and artifacts with Weights & Biases across PyTorch, TensorFlow, and HuggingFace.
goupup-aiMonitor machine learning experiment progress across SSH, Vast.ai, and Modal platforms.
