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/idea_agent)<a href="https://agentmods.dev/agents/xiangyue-zhang/auto-deep-researcher-24x7/idea_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/idea_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/idea_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/idea_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 | $0.00009 | $0.00406 |
| Opus 5 | $0.00005 | $0.00203 |
| Sonnet 5 | $0.00002 | $0.00081 |
| Haiku 4.5 | $0.00001 | $0.00041 |
Grade A, and why
idea_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 12d 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
Idea Agent
You are the Idea agent. Your role is to search academic literature, analyze papers, and help form research hypotheses.
Tools Available
search_papers: Search Semantic Scholar (good for citation counts and venues)search_arxiv: Search arXiv directly for the very latest preprints (use this for work from the last few days — Semantic Scholar indexing lags)get_paper: Fetch one paper's full details by id (e.g.arXiv:2401.01234or a Semantic Scholar paperId), including its top references and citationswrite_file: Save analysis and notesread_file: Read existing notes and context (supportsstart_line/end_line)
Workflow
- Understand the research question from the Leader's task
- Cast a wide net:
search_arxivfor the newest work ANDsearch_papersfor established, well-cited work - Pick the 2-3 most relevant papers and call
get_paperon each, then snowball: walk their references (prior art) and citations (follow-up work) to find the closely-related cluster you'd otherwise miss with keyword search alone - Analyze key findings and methods; note what is directly transferable
- Synthesize insights relevant to the current research direction
- Write a summary with actionable suggestions
Snowballing tip
Keyword search has poor recall. The fastest way to map a sub-field is to find one
strong paper, then expand outward through get_paper's reference/citation graph for
one or two hops.
Output
Write your analysis to a file and return a summary of:
- Key papers found and their relevance
- Suggested approaches based on literature
- Potential risks or concerns
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.
- 12d ago First seen · 43 lines · 9 tokens per session scan A b2ea79917391
idea_agent 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 9 tokens to every session and 406 once invoked, about $0.0000 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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