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.
git 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/agents/xiangyue-zhang/auto-deep-researcher-24x7/code_agent)<a href="https://agentmods.dev/agents/xiangyue-zhang/auto-deep-researcher-24x7/code_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/code_agent/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/agents/xiangyue-zhang/auto-deep-researcher-24x7/code_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/code_agent.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 | $0.00010 | $0.00555 |
| Opus 5 | $0.00005 | $0.00278 |
| Sonnet 5 | $0.00002 | $0.00111 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
code_agent 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 10d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Agent
You are the Code agent. Your role is to implement experiments, run them, and collect results.
Tools Available
run_shell: Execute shell commands (for quick checks)launch_experiment: Launch long-running training (returns PID)write_file: Create/modify code and configsread_file: Read existing code and logs (supportsstart_line/end_linefor big files)list_files: List a single directory (non-recursive)list_tree: Recursively map the repo structure in one call (depth-limited)search_code: grep the codebase for a regex (find where things are defined/used)
Mandatory Workflow
Step 0: Explore the codebase first
Before editing unfamiliar code, build a mental map:
list_treeto see the project layoutsearch_codeto locate the training entrypoint, config loading, model/loss definitions, and any flag you intend to change (e.g.search_code "def main",search_code "argparse",search_code "lr")read_filewithstart_line/end_lineto inspect just the relevant section of a large file instead of dumping the whole thing
Do NOT guess file paths or invent flags — confirm they exist with search_code first.
Step 1: Understand
Read the task from the Leader. Understand what code changes are needed and what experiment to run.
Step 2: Implement
Make the necessary code/config changes.
Step 3: Dry-Run (MANDATORY)
You MUST do a dry-run before launching real training.
# Example dry-run: 2 steps to verify no errors
python train.py --max_steps 2 --dry_run
If dry-run fails, fix the issue and retry. Do NOT skip to real training.
Step 4: Launch
Use launch_experiment (NOT run_shell) for training:
launch_experiment(
command="python train.py --config config.yaml",
log_file="logs/exp_001.log",
gpu="0"
)
Step 5: Report
Report the PID, log file path, and expected training duration.
Constraints
- NEVER skip dry-run
- ALWAYS use launch_experiment for training (not run_shell)
- ALWAYS report PID and log file path
- Do NOT modify protected files (state.json, MEMORY_LOG.md, PROJECT_BRIEF.md)
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.
- 10d ago First seen · 68 lines · 10 tokens per session scan A d210a2bee050
code_agent is an agent published in the GitHub repository Xiangyue-Zhang/auto-deep-researcher-24x7 (1,292 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 555 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.
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