auto-experiment
Launch an autonomous THINK→EXECUTE→REFLECT experiment loop on a GPU project
Check status of running autonomous experiment loops
Claude Code
$ npx -y skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status --agent claude-code
Codex
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a codex -y
Cursor
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a cursor -y
Gemini CLI
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a gemini-cli -y
GitHub Copilot
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a github-copilot -y
OpenCode
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a opencode -y
Windsurf
$ npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-status -a windsurf -y
name: experiment-status description: "Check status of running autonomous experiment loops"
Check the current status of your autonomous experiment agent.
Claude Code: /experiment-status Claude Code: /experiment-status --project /path/to/project Codex: $experiment-status
1. Read `PROJECT_BRIEF.md` — show the research goal 2. Read `MEMORY_LOG.md` — show key results and recent decisions 3. Read `.cycle_counter` — show how many cycles completed 4. Check for running training processes via the configured execution backend 5. If training is running, tail the log file for latest output 6. Show GPU utilization through the configured backend 7. Check if `HUMAN_DIRECTIVE.md` exists (pending directive)
If `execution.mode=ssh`, controller state still comes from the local project directory, but PID checks, training logs, and GPU status come from the configured remote host.
# Experiment Status — my-project ## Goal Train ViT-B/16 on ImageNet to 78%+ accuracy ## Progress - Cycles completed: 4 - Current best: 78.3% (Exp004, ViT-B/16 + cosine + mixup) - Status: TRAINING (PID 12345, GPU 0, running 3.2h) ## Latest Training Log Epoch 45/90 | loss: 2.134 | acc: 77.1% | lr: 1.2e-4 ## Recent Decisions 1. [04-08 14:45] Target reached with mixup, trying stronger augmentation 2. [04-08 06:00] Cosine schedule helped, adding regularization ## Pending Directive None (drop a file at workspace/HUMAN_DIRECTIVE.md to intervene)
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
Launch an autonomous THINK→EXECUTE→REFLECT experiment loop on a GPU project
Daily arXiv paper recommendations with automatic deduplication
Check GPU status, running experiments, and available resources
Refresh Obsidian dashboard and daily notes from current experiment state
Deep analysis of a single paper with figure extraction from arXiv source
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