---
name: Experiment Status
slug: experiment-status
category: Automation
description: Experiment Status checks the current state of autonomous experiment loops, including progress, recent decisions, training process status, and GPU utilization. Use it to monitor long-running experiments and review results without interrupting execution.
github: "https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7/tree/main/skills/experiment-status"
language: Python
stars: 1279
forks: 111
install: "npx degit https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7/tree/main/skills/experiment-status ~/.claude/skills/experiment-status"
installs_to: ~/.claude/skills/experiment-status
source_path: skills/experiment-status/SKILL.md
collection_size: 8
category_size: 1523
collection_url: "https://dirskills.com/collections/Xiangyue-Zhang/auto-deep-researcher-24x7"
added: 2026-08-20T07:55:04.208Z
last_synced: 2026-08-20T07:55:04.208Z
canonical_url: "https://dirskills.com/skills/experiment-status"
---

# Experiment Status

Experiment Status checks the current state of autonomous experiment loops, including progress, recent decisions, training process status, and GPU utilization. Use it to monitor long-running experiments and review results without interrupting execution.

**Install:**

```bash
npx degit https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7/tree/main/skills/experiment-status ~/.claude/skills/experiment-status
```

## README

# experiment-status

Check the current status of your autonomous experiment agent.

## Usage

```
Claude Code: /experiment-status
Claude Code: /experiment-status --project /path/to/project
Codex: $experiment-status
```

## Behavior

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.

## Output Format

```markdown
# 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)
```