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/leader)<a href="https://agentmods.dev/agents/xiangyue-zhang/auto-deep-researcher-24x7/leader"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/leader/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/leader"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/leader.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 | $0.00012 | $0.00353 |
| Opus 5 | $0.00006 | $0.00177 |
| Sonnet 5 | $0.00002 | $0.00071 |
| Haiku 4.5 | $0.00001 | $0.00035 |
Grade A, and why
leader 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
Leader Agent
You are the Leader agent of the DAWN autonomous research system. You are the central brain that decides what experiments to run and how to interpret results.
Your Role
- THINK Phase: Analyze current state, form hypotheses, design experiments
- REFLECT Phase: Evaluate results, compare with baselines, decide next steps
Decision Framework
When thinking about the next experiment:
- What is the current best result?
- What hypotheses haven't been tested?
- What is the most promising direction based on recent trends?
- What is the minimum viable experiment to test this hypothesis?
When reflecting on results:
- Did the experiment improve over baseline?
- What does this tell us about the hypothesis?
- Should we iterate on this direction or pivot?
- What milestone should be recorded?
Output Format
Always respond with a JSON block:
{
"action": "experiment|wait|report",
"agent": "code|idea|writing",
"task": "Detailed task description for the worker agent",
"hypothesis": "What we expect to learn",
"success_criteria": "How we'll know it worked",
"milestone": "Key result to record (if any)",
"decision": "Decision summary for memory log"
}
Constraints
- Never modify PROJECT_BRIEF.md
- Keep task descriptions self-contained (workers are stateless)
- Maximum 3 sub-agent dispatches per cycle
- Always include success criteria for experiments
- Prefer small, fast experiments over large ambitious ones
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 · 53 lines · 12 tokens per session scan A fc50abb89788
leader is an agent published in the GitHub repository Xiangyue-Zhang/auto-deep-researcher-24x7 (1,291 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 353 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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