One-click install
npx skills add https://github.com/dongzhuoyao/deepresearch --skill sue-reset
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sue-reset
Source: https://github.com/dongzhuoyao/deepresearch/tree/main/.codex/skills/sue-reset
Command: npx skills add https://github.com/dongzhuoyao/deepresearch --skill sue-reset

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the pain point of manually cleaning up completed DeepResearch scale-up experiment workspaces, which risks accidental deletion of critical source code, configuration, or historical experiment data, and often leads to inode bloat from scattered loose output files.

Core Features & Use Cases

  • Versioned Experiment Archiving: Automatically moves all epoch artifacts (runtime configs, ledgers, logs, run bundles, local W&B cache) to a structured versioned history directory under sue-exp-history/ for full reproducibility.
  • Safe Workspace Reset: Preserves all source code, Hydra configs, orchestration state, and Git metadata while clearing only experiment-specific outputs, then bootstraps a fresh runtime config for the next experiment epoch.
  • Optional Extended Cleanup: Supports optional deletion of W&B runs and groups for the archived epoch and remote sandbox output roots, with explicit user confirmations for all destructive actions.
  • Use Case: After completing a 3-month LUMI training run for a large language model scale-up experiment, use this skill to archive all run artifacts and start a new experiment with updated hyperparameters without manually sorting through hundreds of output files.

Quick Start

Use the sue-reset skill to archive your current scale-up experiment outputs and initialize a clean workspace for a new experiment epoch.

Frequently Asked Questions about sue-reset

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

FAQPage Schema
How do I safely reset an ML experiment workspace without deleting source code?

To safely reset an ML experiment workspace, you must archive epoch artifacts to a versioned history directory while preserving source code, Hydra configs, and orchestration state. This ensures workspace hygiene and prevents accidental deletion of critical files during epoch transitions.

What is the best way to archive HPC scale-up experiment artifacts on LUMI or Snellius?

The best way to archive HPC scale-up experiment artifacts is moving runtime configs, ledgers, logs, and local W&B cache to a structured versioned history directory. This generates versioned archive manifests ensuring experiment reproducibility across sandbox backends like LUMI and Snellius.

Can I clean up W&B runs and remote sandbox outputs when archiving ML experiments?

Yes, you can clean up W&B runs and remote sandbox outputs during ML experiment archiving. This requires explicit user confirmations for destructive actions, ensuring safe deletion of W&B groups and remote sandbox output roots after archiving completes.

Does workspace reset support sandbox backends like RunPod, AutoDL, and Brev?

Workspace reset supports sandbox backends including RunPod, AutoDL, Brev, LUMI, Snellius, and NM5. It handles versioned archiving across these platforms, enforcing inode-safe output contracts and path contract validation for scale-up ML workflows.

Why does inode bloat happen in scale-up experiment workspaces and how to prevent it?

Inode bloat happens when scattered loose output files accumulate across experiment epochs. Prevent it by archiving artifacts to a structured versioned history directory and enforcing inode-safe output contracts during workspace reset to maintain hygiene across epoch transitions.