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
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/auto-experiment)<a href="https://agentmods.dev/skills/xiangyue-zhang/auto-deep-researcher-24x7/auto-experiment"><img src="https://agentmods.dev/badge/skills/xiangyue-zhang/auto-deep-researcher-24x7/auto-experiment/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/auto-experiment"><img src="https://agentmods.dev/badge/skills/xiangyue-zhang/auto-deep-researcher-24x7/auto-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.01597 |
| Opus 5 | $0.00010 | $0.00798 |
| Sonnet 5 | $0.00004 | $0.00319 |
| Haiku 4.5 | $0.00002 | $0.00160 |
Grade A, and why
auto-experiment 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.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
auto-experiment
Launch an autonomous experiment agent that runs your deep learning experiments 24/7.
What This Does
This skill starts a THINK → EXECUTE → REFLECT loop that:
- Reads your
PROJECT_BRIEF.mdto understand the research goal - Analyzes previous results in
MEMORY_LOG.md - Plans the next experiment (hypothesis + success criteria)
- Implements code changes and runs a mandatory dry-run
- Launches GPU training via
nohup(tracks PID) - Monitors at zero LLM cost (only
kill -0 PID+tail log+nvidia-smi) - Wakes up when training finishes to analyze results
- Updates memory and decides: iterate, pivot, or report
- Repeats
Usage
Claude Code: /auto-experiment
Claude Code: /auto-experiment --project /path/to/my_project --gpu 0
Claude Code: /auto-experiment --project . --max-cycles 5
Codex: $auto-experiment
Prerequisites
The project directory must contain:
PROJECT_BRIEF.md (required)
A frozen reference describing your research goal. Example:
# Goal
Train a ViT-B/16 on ImageNet to reach 78%+ top-1 accuracy.
# Codebase
- Training: train.py
- Config: configs/vit_base.yaml
- Data: /data/imagenet/
# Constraints
- GPU 0-3 available (use DDP)
- Max 90 epochs per run
- Report val accuracy after each run
# Current Best
- ResNet-50 baseline: 76.1%
config.yaml (optional)
Override default agent settings:
agent:
provider: "anthropic" # or "openai" / "claude_cli" / "codex_cli"
model: "claude-sonnet-4-6"
base_url: "" # optional compatible endpoint override
api_key_env: "" # optional custom key env var
auth_token_env: "" # optional custom bearer token env var
max_cycles: -1 # -1 = unlimited
max_steps_per_cycle: 3 # max sub-agent dispatches per cycle
cooldown_interval: 300 # 5 min smart polling
memory:
brief_max_chars: 3000
log_max_chars: 2000
monitor:
poll_interval: 900 # check every 15 min during training
zero_llm: true
experiment:
mandatory_dry_run: true
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 · 177 lines · 20 tokens per session scan A 28bdf2fe05f6
auto-experiment 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 20 tokens to every session and 1,597 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
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
torch-geometric
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
ai-research-reproduction
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…