Auto Deep Researcher 24x7 is an autonomous AI agent that runs and monitors deep learning experiments continuously. Researchers use it to automate experiment execution, including hyperparameter tuning and GPU or Slurm-based workloads. The catalogue add-ons provide agents, skills, and instructions for operating the experiment workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill experiment-statusgit clone --depth 1 https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xiangyue-zhang/auto-deep-researcher-24x7/experiment-status)<a href="https://agentmods.dev/skills/xiangyue-zhang/auto-deep-researcher-24x7/experiment-status"><img src="https://agentmods.dev/badge/skills/xiangyue-zhang/auto-deep-researcher-24x7/experiment-status/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xiangyue-zhang/auto-deep-researcher-24x7/experiment-status"><img src="https://agentmods.dev/badge/skills/xiangyue-zhang/auto-deep-researcher-24x7/experiment-status.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00010 | $0.00399 |
| Opus 5 | $0.00005 | $0.00199 |
| Sonnet 5 | $0.00002 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
experiment-status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
- Read
PROJECT_BRIEF.md— show the research goal - Read
MEMORY_LOG.md— show key results and recent decisions - Read
.cycle_counter— show how many cycles completed - Check for running training processes via the configured execution backend
- If training is running, tail the log file for latest output
- Show GPU utilization through the configured backend
- Check if
HUMAN_DIRECTIVE.mdexists (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
# 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)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 55 lines · 10 tokens per session scan A 8b0aab21e39b
experiment-status is a skill published in the GitHub repository Xiangyue-Zhang/auto-deep-researcher-24x7 (1,291 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 399 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
proof-checker
A mathematical proof review and repair workflow for LaTeX documents. It checks whether a proof has valid reasoning, addresses identified gaps, reviews the fixes, and produces an audit report.
research-lit
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
auto-paper-improvement-loop
An automated editing cycle for a compiled research paper: an external language model reviews it, fixes are applied, and the paper is compiled again.
paper-illustration
A workflow for generating academic illustrations, such as architecture diagrams and method visuals, with image generation and repeated review. Claude plans and checks the figure during the process.
paper-write
A workflow for writing a research paper section by section in LaTeX, a text format used to prepare structured technical documents. It starts from a paper outline and supports several academic venues and citation sources.
experiment-bridge
A workflow that turns an experiment plan into code, reviews the code, runs an initial check, deploys experiments, and collects early results. Deployment means sending the experiment to the environment where it will run, such as a GPU machine.